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Knowledge Router: Learning Disentangled Representations for Knowledge Graphs
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Shuai Zhang, Xi Rao, Yi Tay, Ce Zhang
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The design of expressive representations of entities and relations in a knowledge graph is an important endeavor. While many of the existing approaches have primarily focused on learning from relational patterns and structural information, the intrinsic complexity of KG entities has been more or less overlooked. More concretely, we hypothesize KG entities may be more complex than we think, i.e., an entity may wear many hats and relational triplets may form due to more than a single reason. To this end, this paper proposes to learn disentangled representations of KG entities - a new method that disentangles the inner latent properties of KG entities. Our disentangled process operates at the graph level and a neighborhood mechanism is leveraged to disentangle the hidden properties of each entity. This disentangled representation learning approach is model agnostic and compatible with canonical KG embedding approaches. We conduct extensive experiments on several benchmark datasets, equipping a variety of models (DistMult, SimplE, and QuatE) with our proposed disentangling mechanism. Experimental results demonstrate that our proposed approach substantially improves performance on key metrics.
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https://aclanthology.org/2021.naacl-main.1
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https://aclanthology.org/2021.naacl-main.1.pdf
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NAACL 2021
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Distantly Supervised Relation Extraction with Sentence Reconstruction and Knowledge Base Priors
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Fenia Christopoulou, Makoto Miwa, Sophia Ananiadou
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We propose a multi-task, probabilistic approach to facilitate distantly supervised relation extraction by bringing closer the representations of sentences that contain the same Knowledge Base pairs. To achieve this, we bias the latent space of sentences via a Variational Autoencoder (VAE) that is trained jointly with a relation classifier. The latent code guides the pair representations and influences sentence reconstruction. Experimental results on two datasets created via distant supervision indicate that multi-task learning results in performance benefits. Additional exploration of employing Knowledge Base priors into theVAE reveals that the sentence space can be shifted towards that of the Knowledge Base, offering interpretability and further improving results.
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https://aclanthology.org/2021.naacl-main.2
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https://aclanthology.org/2021.naacl-main.2.pdf
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NAACL 2021
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Cross-Task Instance Representation Interactions and Label Dependencies for Joint Information Extraction with Graph Convolutional Networks
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Minh Van Nguyen, Viet Dac Lai, Thien Huu Nguyen
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Existing works on information extraction (IE) have mainly solved the four main tasks separately (entity mention recognition, relation extraction, event trigger detection, and argument extraction), thus failing to benefit from inter-dependencies between tasks. This paper presents a novel deep learning model to simultaneously solve the four tasks of IE in a single model (called FourIE). Compared to few prior work on jointly performing four IE tasks, FourIE features two novel contributions to capture inter-dependencies between tasks. First, at the representation level, we introduce an interaction graph between instances of the four tasks that is used to enrich the prediction representation for one instance with those from related instances of other tasks. Second, at the label level, we propose a dependency graph for the information types in the four IE tasks that captures the connections between the types expressed in an input sentence. A new regularization mechanism is introduced to enforce the consistency between the golden and predicted type dependency graphs to improve representation learning. We show that the proposed model achieves the state-of-the-art performance for joint IE on both monolingual and multilingual learning settings with three different languages.
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https://aclanthology.org/2021.naacl-main.3
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https://aclanthology.org/2021.naacl-main.3.pdf
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NAACL 2021
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Abstract Meaning Representation Guided Graph Encoding and Decoding for Joint Information Extraction
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Zixuan Zhang, Heng Ji
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The tasks of Rich Semantic Parsing, such as Abstract Meaning Representation (AMR), share similar goals with Information Extraction (IE) to convert natural language texts into structured semantic representations. To take advantage of such similarity, we propose a novel AMR-guided framework for joint information extraction to discover entities, relations, and events with the help of a pre-trained AMR parser. Our framework consists of two novel components: 1) an AMR based semantic graph aggregator to let the candidate entity and event trigger nodes collect neighborhood information from AMR graph for passing message among related knowledge elements; 2) an AMR guided graph decoder to extract knowledge elements based on the order decided by the hierarchical structures in AMR. Experiments on multiple datasets have shown that the AMR graph encoder and decoder have provided significant gains and our approach has achieved new state-of-the-art performance on all IE subtasks.
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https://aclanthology.org/2021.naacl-main.4
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https://aclanthology.org/2021.naacl-main.4.pdf
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NAACL 2021
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A Frustratingly Easy Approach for Entity and Relation Extraction
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Zexuan Zhong, Danqi Chen
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End-to-end relation extraction aims to identify named entities and extract relations between them. Most recent work models these two subtasks jointly, either by casting them in one structured prediction framework, or performing multi-task learning through shared representations. In this work, we present a simple pipelined approach for entity and relation extraction, and establish the new state-of-the-art on standard benchmarks (ACE04, ACE05 and SciERC), obtaining a 1.7%-2.8% absolute improvement in relation F1 over previous joint models with the same pre-trained encoders. Our approach essentially builds on two independent encoders and merely uses the entity model to construct the input for the relation model. Through a series of careful examinations, we validate the importance of learning distinct contextual representations for entities and relations, fusing entity information early in the relation model, and incorporating global context. Finally, we also present an efficient approximation to our approach which requires only one pass of both entity and relation encoders at inference time, achieving an 8-16× speedup with a slight reduction in accuracy.
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https://aclanthology.org/2021.naacl-main.5
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https://aclanthology.org/2021.naacl-main.5.pdf
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NAACL 2021
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Event Time Extraction and Propagation via Graph Attention Networks
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Haoyang Wen, Yanru Qu, Heng Ji, Qiang Ning, Jiawei Han, Avi Sil, Hanghang Tong, Dan Roth
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Grounding events into a precise timeline is important for natural language understanding but has received limited attention in recent work. This problem is challenging due to the inherent ambiguity of language and the requirement for information propagation over inter-related events. This paper first formulates this problem based on a 4-tuple temporal representation used in entity slot filling, which allows us to represent fuzzy time spans more conveniently. We then propose a graph attention network-based approach to propagate temporal information over document-level event graphs constructed by shared entity arguments and temporal relations. To better evaluate our approach, we present a challenging new benchmark on the ACE2005 corpus, where more than 78% of events do not have time spans mentioned explicitly in their local contexts. The proposed approach yields an absolute gain of 7.0% in match rate over contextualized embedding approaches, and 16.3% higher match rate compared to sentence-level manual event time argument annotation.
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https://aclanthology.org/2021.naacl-main.6
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https://aclanthology.org/2021.naacl-main.6.pdf
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NAACL 2021
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Probing Word Translations in the Transformer and Trading Decoder for Encoder Layers
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Hongfei Xu, Josef van Genabith, Qiuhui Liu, Deyi Xiong
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Due to its effectiveness and performance, the Transformer translation model has attracted wide attention, most recently in terms of probing-based approaches. Previous work focuses on using or probing source linguistic features in the encoder. To date, the way word translation evolves in Transformer layers has not yet been investigated. Naively, one might assume that encoder layers capture source information while decoder layers translate. In this work, we show that this is not quite the case: translation already happens progressively in encoder layers and even in the input embeddings. More surprisingly, we find that some of the lower decoder layers do not actually do that much decoding. We show all of this in terms of a probing approach where we project representations of the layer analyzed to the final trained and frozen classifier level of the Transformer decoder to measure word translation accuracy. Our findings motivate and explain a Transformer configuration change: if translation already happens in the encoder layers, perhaps we can increase the number of encoder layers, while decreasing the number of decoder layers, boosting decoding speed, without loss in translation quality? Our experiments show that this is indeed the case: we can increase speed by up to a factor 2.3 with small gains in translation quality, while an 18-4 deep encoder configuration boosts translation quality by +1.42 BLEU (En-De) at a speed-up of 1.4.
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https://aclanthology.org/2021.naacl-main.7
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https://aclanthology.org/2021.naacl-main.7.pdf
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NAACL 2021
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Mediators in Determining what Processing BERT Performs First
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Aviv Slobodkin, Leshem Choshen, Omri Abend
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Probing neural models for the ability to perform downstream tasks using their activation patterns is often used to localize what parts of the network specialize in performing what tasks. However, little work addressed potential mediating factors in such comparisons. As a test-case mediating factor, we consider the prediction’s context length, namely the length of the span whose processing is minimally required to perform the prediction. We show that not controlling for context length may lead to contradictory conclusions as to the localization patterns of the network, depending on the distribution of the probing dataset. Indeed, when probing BERT with seven tasks, we find that it is possible to get 196 different rankings between them when manipulating the distribution of context lengths in the probing dataset. We conclude by presenting best practices for conducting such comparisons in the future.
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https://aclanthology.org/2021.naacl-main.8
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https://aclanthology.org/2021.naacl-main.8.pdf
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NAACL 2021
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Automatic Generation of Contrast Sets from Scene Graphs: Probing the Compositional Consistency of GQA
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Yonatan Bitton, Gabriel Stanovsky, Roy Schwartz, Michael Elhadad
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Recent works have shown that supervised models often exploit data artifacts to achieve good test scores while their performance severely degrades on samples outside their training distribution. Contrast sets (Gardneret al., 2020) quantify this phenomenon by perturbing test samples in a minimal way such that the output label is modified. While most contrast sets were created manually, requiring intensive annotation effort, we present a novel method which leverages rich semantic input representation to automatically generate contrast sets for the visual question answering task. Our method computes the answer of perturbed questions, thus vastly reducing annotation cost and enabling thorough evaluation of models’ performance on various semantic aspects (e.g., spatial or relational reasoning). We demonstrate the effectiveness of our approach on the GQA dataset and its semantic scene graph image representation. We find that, despite GQA’s compositionality and carefully balanced label distribution, two high-performing models drop 13-17% in accuracy compared to the original test set. Finally, we show that our automatic perturbation can be applied to the training set to mitigate the degradation in performance, opening the door to more robust models.
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https://aclanthology.org/2021.naacl-main.9
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https://aclanthology.org/2021.naacl-main.9.pdf
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NAACL 2021
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Multilingual Language Models Predict Human Reading Behavior
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Nora Hollenstein, Federico Pirovano, Ce Zhang, Lena Jäger, Lisa Beinborn
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We analyze if large language models are able to predict patterns of human reading behavior. We compare the performance of language-specific and multilingual pretrained transformer models to predict reading time measures reflecting natural human sentence processing on Dutch, English, German, and Russian texts. This results in accurate models of human reading behavior, which indicates that transformer models implicitly encode relative importance in language in a way that is comparable to human processing mechanisms. We find that BERT and XLM models successfully predict a range of eye tracking features. In a series of experiments, we analyze the cross-domain and cross-language abilities of these models and show how they reflect human sentence processing.
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https://aclanthology.org/2021.naacl-main.10
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https://aclanthology.org/2021.naacl-main.10.pdf
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NAACL 2021
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Do Syntactic Probes Probe Syntax? Experiments with Jabberwocky Probing
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Rowan Hall Maudslay, Ryan Cotterell
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Analysing whether neural language models encode linguistic information has become popular in NLP. One method of doing so, which is frequently cited to support the claim that models like BERT encode syntax, is called probing; probes are small supervised models trained to extract linguistic information from another model’s output. If a probe is able to predict a particular structure, it is argued that the model whose output it is trained on must have implicitly learnt to encode it. However, drawing a generalisation about a model’s linguistic knowledge about a specific phenomena based on what a probe is able to learn may be problematic: in this work, we show that semantic cues in training data means that syntactic probes do not properly isolate syntax. We generate a new corpus of semantically nonsensical but syntactically well-formed Jabberwocky sentences, which we use to evaluate two probes trained on normal data. We train the probes on several popular language models (BERT, GPT-2, and RoBERTa), and find that in all settings they perform worse when evaluated on these data, for one probe by an average of 15.4 UUAS points absolute. Although in most cases they still outperform the baselines, their lead is reduced substantially, e.g. by 53% in the case of BERT for one probe. This begs the question: what empirical scores constitute knowing syntax?
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https://aclanthology.org/2021.naacl-main.11
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https://aclanthology.org/2021.naacl-main.11.pdf
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NAACL 2021
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A Non-Linear Structural Probe
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Jennifer C. White, Tiago Pimentel, Naomi Saphra, Ryan Cotterell
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Probes are models devised to investigate the encoding of knowledge—e.g. syntactic structure—in contextual representations. Probes are often designed for simplicity, which has led to restrictions on probe design that may not allow for the full exploitation of the structure of encoded information; one such restriction is linearity. We examine the case of a structural probe (Hewitt and Manning, 2019), which aims to investigate the encoding of syntactic structure in contextual representations through learning only linear transformations. By observing that the structural probe learns a metric, we are able to kernelize it and develop a novel non-linear variant with an identical number of parameters. We test on 6 languages and find that the radial-basis function (RBF) kernel, in conjunction with regularization, achieves a statistically significant improvement over the baseline in all languages—implying that at least part of the syntactic knowledge is encoded non-linearly. We conclude by discussing how the RBF kernel resembles BERT’s self-attention layers and speculate that this resemblance leads to the RBF-based probe’s stronger performance.
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https://aclanthology.org/2021.naacl-main.12
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https://aclanthology.org/2021.naacl-main.12.pdf
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NAACL 2021
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Concealed Data Poisoning Attacks on NLP Models
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Eric Wallace, Tony Zhao, Shi Feng, Sameer Singh
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Adversarial attacks alter NLP model predictions by perturbing test-time inputs. However, it is much less understood whether, and how, predictions can be manipulated with small, concealed changes to the training data. In this work, we develop a new data poisoning attack that allows an adversary to control model predictions whenever a desired trigger phrase is present in the input. For instance, we insert 50 poison examples into a sentiment model’s training set that causes the model to frequently predict Positive whenever the input contains “James Bond”. Crucially, we craft these poison examples using a gradient-based procedure so that they do not mention the trigger phrase. We also apply our poison attack to language modeling (“Apple iPhone” triggers negative generations) and machine translation (“iced coffee” mistranslated as “hot coffee”). We conclude by proposing three defenses that can mitigate our attack at some cost in prediction accuracy or extra human annotation.
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https://aclanthology.org/2021.naacl-main.13
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https://aclanthology.org/2021.naacl-main.13.pdf
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NAACL 2021
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Backtranslation Feedback Improves User Confidence in MT, Not Quality
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Vilém Zouhar, Michal Novák, Matúš Žilinec, Ondřej Bojar, Mateo Obregón, Robin L. Hill, Frédéric Blain, Marina Fomicheva, Lucia Specia, Lisa Yankovskaya
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Translating text into a language unknown to the text’s author, dubbed outbound translation, is a modern need for which the user experience has significant room for improvement, beyond the basic machine translation facility. We demonstrate this by showing three ways in which user confidence in the outbound translation, as well as its overall final quality, can be affected: backward translation, quality estimation (with alignment) and source paraphrasing. In this paper, we describe an experiment on outbound translation from English to Czech and Estonian. We examine the effects of each proposed feedback module and further focus on how the quality of machine translation systems influence these findings and the user perception of success. We show that backward translation feedback has a mixed effect on the whole process: it increases user confidence in the produced translation, but not the objective quality.
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https://aclanthology.org/2021.naacl-main.14
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https://aclanthology.org/2021.naacl-main.14.pdf
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NAACL 2021
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Data Filtering using Cross-Lingual Word Embeddings
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Christian Herold, Jan Rosendahl, Joris Vanvinckenroye, Hermann Ney
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Data filtering for machine translation (MT) describes the task of selecting a subset of a given, possibly noisy corpus with the aim to maximize the performance of an MT system trained on this selected data. Over the years, many different filtering approaches have been proposed. However, varying task definitions and data conditions make it difficult to draw a meaningful comparison. In the present work, we aim for a more systematic approach to the task at hand. First, we analyze the performance of language identification, a tool commonly used for data filtering in the MT community and identify specific weaknesses. Based on our findings, we then propose several novel methods for data filtering, based on cross-lingual word embeddings. We compare our approaches to one of the winning methods from the WMT 2018 shared task on parallel corpus filtering on three real-life, high resource MT tasks. We find that said method, which was performing very strong in the WMT shared task, does not perform well within our more realistic task conditions. While we find that our approaches come out at the top on all three tasks, different variants perform best on different tasks. Further experiments on the WMT 2020 shared task for parallel corpus filtering show that our methods achieve comparable results to the strongest submissions of this campaign.
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https://aclanthology.org/2021.naacl-main.15
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https://aclanthology.org/2021.naacl-main.15.pdf
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NAACL 2021
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Improving the Lexical Ability of Pretrained Language Models for Unsupervised Neural Machine Translation
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Alexandra Chronopoulou, Dario Stojanovski, Alexander Fraser
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Successful methods for unsupervised neural machine translation (UNMT) employ cross-lingual pretraining via self-supervision, often in the form of a masked language modeling or a sequence generation task, which requires the model to align the lexical- and high-level representations of the two languages. While cross-lingual pretraining works for similar languages with abundant corpora, it performs poorly in low-resource and distant languages. Previous research has shown that this is because the representations are not sufficiently aligned. In this paper, we enhance the bilingual masked language model pretraining with lexical-level information by using type-level cross-lingual subword embeddings. Empirical results demonstrate improved performance both on UNMT (up to 4.5 BLEU) and bilingual lexicon induction using our method compared to a UNMT baseline.
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https://aclanthology.org/2021.naacl-main.16
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https://aclanthology.org/2021.naacl-main.16.pdf
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NAACL 2021
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Neural Machine Translation without Embeddings
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Uri Shaham, Omer Levy
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Many NLP models operate over sequences of subword tokens produced by hand-crafted tokenization rules and heuristic subword induction algorithms. A simple universal alternative is to represent every computerized text as a sequence of bytes via UTF-8, obviating the need for an embedding layer since there are fewer token types (256) than dimensions. Surprisingly, replacing the ubiquitous embedding layer with one-hot representations of each byte does not hurt performance; experiments on byte-to-byte machine translation from English to 10 different languages show a consistent improvement in BLEU, rivaling character-level and even standard subword-level models. A deeper investigation reveals that the combination of embeddingless models with decoder-input dropout amounts to token dropout, which benefits byte-to-byte models in particular.
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https://aclanthology.org/2021.naacl-main.17
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https://aclanthology.org/2021.naacl-main.17.pdf
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NAACL 2021
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Counterfactual Data Augmentation for Neural Machine Translation
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Qi Liu, Matt Kusner, Phil Blunsom
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We propose a data augmentation method for neural machine translation. It works by interpreting language models and phrasal alignment causally. Specifically, it creates augmented parallel translation corpora by generating (path-specific) counterfactual aligned phrases. We generate these by sampling new source phrases from a masked language model, then sampling an aligned counterfactual target phrase by noting that a translation language model can be interpreted as a Gumbel-Max Structural Causal Model (Oberst and Sontag, 2019). Compared to previous work, our method takes both context and alignment into account to maintain the symmetry between source and target sequences. Experiments on IWSLT’15 English → Vietnamese, WMT’17 English → German, WMT’18 English → Turkish, and WMT’19 robust English → French show that the method can improve the performance of translation, backtranslation and translation robustness.
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https://aclanthology.org/2021.naacl-main.18
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https://aclanthology.org/2021.naacl-main.18.pdf
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NAACL 2021
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Cultural and Geographical Influences on Image Translatability of Words across Languages
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Nikzad Khani, Isidora Tourni, Mohammad Sadegh Rasooli, Chris Callison-Burch, Derry Tanti Wijaya
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Neural Machine Translation (NMT) models have been observed to produce poor translations when there are few/no parallel sentences to train the models. In the absence of parallel data, several approaches have turned to the use of images to learn translations. Since images of words, e.g., horse may be unchanged across languages, translations can be identified via images associated with words in different languages that have a high degree of visual similarity. However, translating via images has been shown to improve upon text-only models only marginally. To better understand when images are useful for translation, we study image translatability of words, which we define as the translatability of words via images, by measuring intra- and inter-cluster similarities of image representations of words that are translations of each other. We find that images of words are not always invariant across languages, and that language pairs with shared culture, meaning having either a common language family, ethnicity or religion, have improved image translatability (i.e., have more similar images for similar words) compared to its converse, regardless of their geographic proximity. In addition, in line with previous works that show images help more in translating concrete words, we found that concrete words have improved image translatability compared to abstract ones.
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https://aclanthology.org/2021.naacl-main.19
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https://aclanthology.org/2021.naacl-main.19.pdf
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NAACL 2021
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Multilingual BERT Post-Pretraining Alignment
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Lin Pan, Chung-Wei Hang, Haode Qi, Abhishek Shah, Saloni Potdar, Mo Yu
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We propose a simple method to align multilingual contextual embeddings as a post-pretraining step for improved cross-lingual transferability of the pretrained language models. Using parallel data, our method aligns embeddings on the word level through the recently proposed Translation Language Modeling objective as well as on the sentence level via contrastive learning and random input shuffling. We also perform sentence-level code-switching with English when finetuning on downstream tasks. On XNLI, our best model (initialized from mBERT) improves over mBERT by 4.7% in the zero-shot setting and achieves comparable result to XLM for translate-train while using less than 18% of the same parallel data and 31% fewer model parameters. On MLQA, our model outperforms XLM-R_Base, which has 57% more parameters than ours.
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https://aclanthology.org/2021.naacl-main.20
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https://aclanthology.org/2021.naacl-main.20.pdf
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NAACL 2021
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A Million Tweets Are Worth a Few Points: Tuning Transformers for Customer Service Tasks
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Amir Hadifar, Sofie Labat, Veronique Hoste, Chris Develder, Thomas Demeester
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In online domain-specific customer service applications, many companies struggle to deploy advanced NLP models successfully, due to the limited availability of and noise in their datasets. While prior research demonstrated the potential of migrating large open-domain pretrained models for domain-specific tasks, the appropriate (pre)training strategies have not yet been rigorously evaluated in such social media customer service settings, especially under multilingual conditions. We address this gap by collecting a multilingual social media corpus containing customer service conversations (865k tweets), comparing various pipelines of pretraining and finetuning approaches, applying them on 5 different end tasks. We show that pretraining a generic multilingual transformer model on our in-domain dataset, before finetuning on specific end tasks, consistently boosts performance, especially in non-English settings.
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https://aclanthology.org/2021.naacl-main.21
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https://aclanthology.org/2021.naacl-main.21.pdf
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NAACL 2021
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Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases
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Ilias Chalkidis, Manos Fergadiotis, Dimitrios Tsarapatsanis, Nikolaos Aletras, Ion Androutsopoulos, Prodromos Malakasiotis
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Interpretability or explainability is an emerging research field in NLP. From a user-centric point of view, the goal is to build models that provide proper justification for their decisions, similar to those of humans, by requiring the models to satisfy additional constraints. To this end, we introduce a new application on legal text where, contrary to mainstream literature targeting word-level rationales, we conceive rationales as selected paragraphs in multi-paragraph structured court cases. We also release a new dataset comprising European Court of Human Rights cases, including annotations for paragraph-level rationales. We use this dataset to study the effect of already proposed rationale constraints, i.e., sparsity, continuity, and comprehensiveness, formulated as regularizers. Our findings indicate that some of these constraints are not beneficial in paragraph-level rationale extraction, while others need re-formulation to better handle the multi-label nature of the task we consider. We also introduce a new constraint, singularity, which further improves the quality of rationales, even compared with noisy rationale supervision. Experimental results indicate that the newly introduced task is very challenging and there is a large scope for further research.
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https://aclanthology.org/2021.naacl-main.22
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https://aclanthology.org/2021.naacl-main.22.pdf
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NAACL 2021
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Answering Product-Questions by Utilizing Questions from Other Contextually Similar Products
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Ohad Rozen, David Carmel, Avihai Mejer, Vitaly Mirkis, Yftah Ziser
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Predicting the answer to a product-related question is an emerging field of research that recently attracted a lot of attention. Answering subjective and opinion-based questions is most challenging due to the dependency on customer generated content. Previous works mostly focused on review-aware answer prediction; however, these approaches fail for new or unpopular products, having no (or only a few) reviews at hand. In this work, we propose a novel and complementary approach for predicting the answer for such questions, based on the answers for similar questions asked on similar products. We measure the contextual similarity between products based on the answers they provide for the same question. A mixture-of-expert framework is used to predict the answer by aggregating the answers from contextually similar products. Empirical results demonstrate that our model outperforms strong baselines on some segments of questions, namely those that have roughly ten or more similar resolved questions in the corpus. We additionally publish two large-scale datasets used in this work, one is of similar product question pairs, and the second is of product question-answer pairs.
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https://aclanthology.org/2021.naacl-main.23
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https://aclanthology.org/2021.naacl-main.23.pdf
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NAACL 2021
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EnSidNet: Enhanced Hybrid Siamese-Deep Network for grouping clinical trials into drug-development pathways
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Lucia Pagani
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Siamese Neural Networks have been widely used to perform similarity classification in multi-class settings. Their architecture can be used to group the clinical trials belonging to the same drug-development pathway along the several clinical trial phases. Here we present an approach for the unmet need of drug-development pathway reconstruction, based on an Enhanced hybrid Siamese-Deep Neural Network (EnSidNet). The proposed model demonstrates significant improvement above baselines in a 1-shot evaluation setting and in a classical similarity setting. EnSidNet can be an essential tool in a semi-supervised learning environment: by selecting clinical trials highly likely to belong to the same drug-development pathway it is possible to speed up the labelling process of human experts, allowing the check of a consistent volume of data, further used in the model’s training dataset.
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https://aclanthology.org/2021.naacl-main.24
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https://aclanthology.org/2021.naacl-main.24.pdf
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NAACL 2021
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DATE: Detecting Anomalies in Text via Self-Supervision of Transformers
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Andrei Manolache, Florin Brad, Elena Burceanu
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Leveraging deep learning models for Anomaly Detection (AD) has seen widespread use in recent years due to superior performances over traditional methods. Recent deep methods for anomalies in images learn better features of normality in an end-to-end self-supervised setting. These methods train a model to discriminate between different transformations applied to visual data and then use the output to compute an anomaly score. We use this approach for AD in text, by introducing a novel pretext task on text sequences. We learn our DATE model end-to-end, enforcing two independent and complementary self-supervision signals, one at the token-level and one at the sequence-level. Under this new task formulation, we show strong quantitative and qualitative results on the 20Newsgroups and AG News datasets. In the semi-supervised setting, we outperform state-of-the-art results by +13.5% and +6.9%, respectively (AUROC). In the unsupervised configuration, DATE surpasses all other methods even when 10% of its training data is contaminated with outliers (compared with 0% for the others).
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https://aclanthology.org/2021.naacl-main.25
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https://aclanthology.org/2021.naacl-main.25.pdf
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NAACL 2021
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A Simple Approach for Handling Out-of-Vocabulary Identifiers in Deep Learning for Source Code
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Nadezhda Chirkova, Sergey Troshin
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There is an emerging interest in the application of natural language processing models to source code processing tasks. One of the major problems in applying deep learning to software engineering is that source code often contains a lot of rare identifiers, resulting in huge vocabularies. We propose a simple, yet effective method, based on identifier anonymization, to handle out-of-vocabulary (OOV) identifiers. Our method can be treated as a preprocessing step and, therefore, allows for easy implementation. We show that the proposed OOV anonymization method significantly improves the performance of the Transformer in two code processing tasks: code completion and bug fixing.
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https://aclanthology.org/2021.naacl-main.26
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https://aclanthology.org/2021.naacl-main.26.pdf
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NAACL 2021
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Fast and Scalable Dialogue State Tracking with Explicit Modular Decomposition
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Dingmin Wang, Chenghua Lin, Qi Liu, Kam-Fai Wong
|
We present a fast and scalable architecture called Explicit Modular Decomposition (EMD), in which we incorporate both classification-based and extraction-based methods and design four modules (for clas- sification and sequence labelling) to jointly extract dialogue states. Experimental results based on the MultiWoz 2.0 dataset validates the superiority of our proposed model in terms of both complexity and scalability when compared to the state-of-the-art methods, especially in the scenario of multi-domain dialogues entangled with many turns of utterances.
|
https://aclanthology.org/2021.naacl-main.27
|
https://aclanthology.org/2021.naacl-main.27.pdf
|
NAACL 2021
|
|||
Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks
|
Nandan Thakur, Nils Reimers, Johannes Daxenberger, Iryna Gurevych
|
There are two approaches for pairwise sentence scoring: Cross-encoders, which perform full-attention over the input pair, and Bi-encoders, which map each input independently to a dense vector space. While cross-encoders often achieve higher performance, they are too slow for many practical use cases. Bi-encoders, on the other hand, require substantial training data and fine-tuning over the target task to achieve competitive performance. We present a simple yet efficient data augmentation strategy called Augmented SBERT, where we use the cross-encoder to label a larger set of input pairs to augment the training data for the bi-encoder. We show that, in this process, selecting the sentence pairs is non-trivial and crucial for the success of the method. We evaluate our approach on multiple tasks (in-domain) as well as on a domain adaptation task. Augmented SBERT achieves an improvement of up to 6 points for in-domain and of up to 37 points for domain adaptation tasks compared to the original bi-encoder performance.
|
https://aclanthology.org/2021.naacl-main.28
|
https://aclanthology.org/2021.naacl-main.28.pdf
|
NAACL 2021
|
|||
SmBoP: Semi-autoregressive Bottom-up Semantic Parsing
|
Ohad Rubin, Jonathan Berant
|
The de-facto standard decoding method for semantic parsing in recent years has been to autoregressively decode the abstract syntax tree of the target program using a top-down depth-first traversal. In this work, we propose an alternative approach: a Semi-autoregressive Bottom-up Parser (SmBoP) that constructs at decoding step t the top-K sub-trees of height ≤ t. Our parser enjoys several benefits compared to top-down autoregressive parsing. From an efficiency perspective, bottom-up parsing allows to decode all sub-trees of a certain height in parallel, leading to logarithmic runtime complexity rather than linear. From a modeling perspective, a bottom-up parser learns representations for meaningful semantic sub-programs at each step, rather than for semantically-vacuous partial trees. We apply SmBoP on Spider, a challenging zero-shot semantic parsing benchmark, and show that SmBoP leads to a 2.2x speed-up in decoding time and a ~5x speed-up in training time, compared to a semantic parser that uses autoregressive decoding. SmBoP obtains 71.1 denotation accuracy on Spider, establishing a new state-of-the-art, and 69.5 exact match, comparable to the 69.6 exact match of the autoregressive RAT-SQL+GraPPa.
|
https://aclanthology.org/2021.naacl-main.29
|
https://aclanthology.org/2021.naacl-main.29.pdf
|
NAACL 2021
|
|||
SGL: Speaking the Graph Languages of Semantic Parsing via Multilingual Translation
|
Luigi Procopio, Rocco Tripodi, Roberto Navigli
|
{'url': 'https://github.com/SapienzaNLP/sgl', '#text': 'Graph-based semantic parsing aims to represent textual meaning through directed graphs. As one of the most promising general-purpose meaning representations, these structures and their parsing have gained a significant interest momentum during recent years, with several diverse formalisms being proposed. Yet, owing to this very heterogeneity, most of the research effort has focused mainly on solutions specific to a given formalism. In this work, instead, we reframe semantic parsing towards multiple formalisms as Multilingual Neural Machine Translation (MNMT), and propose SGL, a many-to-many seq2seq architecture trained with an MNMT objective. Backed by several experiments, we show that this framework is indeed effective once the learning procedure is enhanced with large parallel corpora coming from Machine Translation: we report competitive performances on AMR and UCCA parsing, especially once paired with pre-trained architectures. Furthermore, we find that models trained under this configuration scale remarkably well to tasks such as cross-lingual AMR parsing: SGL outperforms all its competitors by a large margin without even explicitly seeing non-English to AMR examples at training time and, once these examples are included as well, sets an unprecedented state of the art in this task. We release our code and our models for research purposes at .'}
|
https://aclanthology.org/2021.naacl-main.30
|
https://aclanthology.org/2021.naacl-main.30.pdf
|
NAACL 2021
|
|||
Unifying Cross-Lingual Semantic Role Labeling with Heterogeneous Linguistic Resources
|
Simone Conia, Andrea Bacciu, Roberto Navigli
|
{'url': 'https://github.com/SapienzaNLP/unify-srl', '#text': 'While cross-lingual techniques are finding increasing success in a wide range of Natural Language Processing tasks, their application to Semantic Role Labeling (SRL) has been strongly limited by the fact that each language adopts its own linguistic formalism, from PropBank for English to AnCora for Spanish and PDT-Vallex for Czech, inter alia. In this work, we address this issue and present a unified model to perform cross-lingual SRL over heterogeneous linguistic resources. Our model implicitly learns a high-quality mapping for different formalisms across diverse languages without resorting to word alignment and/or translation techniques. We find that, not only is our cross-lingual system competitive with the current state of the art but that it is also robust to low-data scenarios. Most interestingly, our unified model is able to annotate a sentence in a single forward pass with all the inventories it was trained with, providing a tool for the analysis and comparison of linguistic theories across different languages. We release our code and model at .'}
|
https://aclanthology.org/2021.naacl-main.31
|
https://aclanthology.org/2021.naacl-main.31.pdf
|
NAACL 2021
|
|||
Fool Me Twice: Entailment from Wikipedia Gamification
|
Julian Eisenschlos, Bhuwan Dhingra, Jannis Bulian, Benjamin Börschinger, Jordan Boyd-Graber
|
We release FoolMeTwice (FM2 for short), a large dataset of challenging entailment pairs collected through a fun multi-player game. Gamification encourages adversarial examples, drastically lowering the number of examples that can be solved using “shortcuts” compared to other popular entailment datasets. Players are presented with two tasks. The first task asks the player to write a plausible claim based on the evidence from a Wikipedia page. The second one shows two plausible claims written by other players, one of which is false, and the goal is to identify it before the time runs out. Players “pay” to see clues retrieved from the evidence pool: the more evidence the player needs, the harder the claim. Game-play between motivated players leads to diverse strategies for crafting claims, such as temporal inference and diverting to unrelated evidence, and results in higher quality data for the entailment and evidence retrieval tasks. We open source the dataset and the game code.
|
https://aclanthology.org/2021.naacl-main.32
|
https://aclanthology.org/2021.naacl-main.32.pdf
|
NAACL 2021
|
|||
Meta-Learning for Domain Generalization in Semantic Parsing
|
Bailin Wang, Mirella Lapata, Ivan Titov
|
The importance of building semantic parsers which can be applied to new domains and generate programs unseen at training has long been acknowledged, and datasets testing out-of-domain performance are becoming increasingly available. However, little or no attention has been devoted to learning algorithms or objectives which promote domain generalization, with virtually all existing approaches relying on standard supervised learning. In this work, we use a meta-learning framework which targets zero-shot domain generalization for semantic parsing. We apply a model-agnostic training algorithm that simulates zero-shot parsing by constructing virtual train and test sets from disjoint domains. The learning objective capitalizes on the intuition that gradient steps that improve source-domain performance should also improve target-domain performance, thus encouraging a parser to generalize to unseen target domains. Experimental results on the (English) Spider and Chinese Spider datasets show that the meta-learning objective significantly boosts the performance of a baseline parser.
|
https://aclanthology.org/2021.naacl-main.33
|
https://aclanthology.org/2021.naacl-main.33.pdf
|
NAACL 2021
|
|||
Aspect-Controlled Neural Argument Generation
|
Benjamin Schiller, Johannes Daxenberger, Iryna Gurevych
|
We rely on arguments in our daily lives to deliver our opinions and base them on evidence, making them more convincing in turn. However, finding and formulating arguments can be challenging. In this work, we present the Arg-CTRL - a language model for argument generation that can be controlled to generate sentence-level arguments for a given topic, stance, and aspect. We define argument aspect detection as a necessary method to allow this fine-granular control and crowdsource a dataset with 5,032 arguments annotated with aspects. Our evaluation shows that the Arg-CTRL is able to generate high-quality, aspect-specific arguments, applicable to automatic counter-argument generation. We publish the model weights and all datasets and code to train the Arg-CTRL.
|
https://aclanthology.org/2021.naacl-main.34
|
https://aclanthology.org/2021.naacl-main.34.pdf
|
NAACL 2021
|
|||
Text Generation from Discourse Representation Structures
|
Jiangming Liu, Shay B. Cohen, Mirella Lapata
|
We propose neural models to generate text from formal meaning representations based on Discourse Representation Structures (DRSs). DRSs are document-level representations which encode rich semantic detail pertaining to rhetorical relations, presupposition, and co-reference within and across sentences. We formalize the task of neural DRS-to-text generation and provide modeling solutions for the problems of condition ordering and variable naming which render generation from DRSs non-trivial. Our generator relies on a novel sibling treeLSTM model which is able to accurately represent DRS structures and is more generally suited to trees with wide branches. We achieve competitive performance (59.48 BLEU) on the GMB benchmark against several strong baselines.
|
https://aclanthology.org/2021.naacl-main.35
|
https://aclanthology.org/2021.naacl-main.35.pdf
|
NAACL 2021
|
|||
APo-VAE: Text Generation in Hyperbolic Space
|
Shuyang Dai, Zhe Gan, Yu Cheng, Chenyang Tao, Lawrence Carin, Jingjing Liu
|
Natural language often exhibits inherent hierarchical structure ingrained with complex syntax and semantics. However, most state-of-the-art deep generative models learn embeddings only in Euclidean vector space, without accounting for this structural property of language. In this paper, we investigate text generation in a hyperbolic latent space to learn continuous hierarchical representations. An Adversarial Poincare Variational Autoencoder (APo-VAE) is presented, where both the prior and variational posterior of latent variables are defined over a Poincare ball via wrapped normal distributions. By adopting the primal-dual formulation of Kullback-Leibler divergence, an adversarial learning procedure is introduced to empower robust model training. Extensive experiments in language modeling, unaligned style transfer, and dialog-response generation demonstrate the effectiveness of the proposed APo-VAE model over VAEs in Euclidean latent space, thanks to its superb capabilities in capturing latent language hierarchies in hyperbolic space.
|
https://aclanthology.org/2021.naacl-main.36
|
https://aclanthology.org/2021.naacl-main.36.pdf
|
NAACL 2021
|
|||
DART: Open-Domain Structured Data Record to Text Generation
|
Linyong Nan, Dragomir Radev, Rui Zhang, Amrit Rau, Abhinand Sivaprasad, Chiachun Hsieh, Xiangru Tang, Aadit Vyas, Neha Verma, Pranav Krishna, Yangxiaokang Liu, Nadia Irwanto, Jessica Pan, Faiaz Rahman, Ahmad Zaidi, Mutethia Mutuma, Yasin Tarabar, Ankit Gupta, Tao Yu, Yi Chern Tan, Xi Victoria Lin, Caiming Xiong, Richard Socher, Nazneen Fatema Rajani
|
{'url': 'https://github.com/Yale-LILY/dart', '#text': 'We present DART, an open domain structured DAta Record to Text generation dataset with over 82k instances (DARTs). Data-to-text annotations can be a costly process, especially when dealing with tables which are the major source of structured data and contain nontrivial structures. To this end, we propose a procedure of extracting semantic triples from tables that encodes their structures by exploiting the semantic dependencies among table headers and the table title. Our dataset construction framework effectively merged heterogeneous sources from open domain semantic parsing and spoken dialogue systems by utilizing techniques including tree ontology annotation, question-answer pair to declarative sentence conversion, and predicate unification, all with minimum post-editing. We present systematic evaluation on DART as well as new state-of-the-art results on WebNLG 2017 to show that DART (1) poses new challenges to existing data-to-text datasets and (2) facilitates out-of-domain generalization. Our data and code can be found at .'}
|
https://aclanthology.org/2021.naacl-main.37
|
https://aclanthology.org/2021.naacl-main.37.pdf
|
NAACL 2021
|
|||
When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models
|
Benjamin Muller, Antonios Anastasopoulos, Benoît Sagot, Djamé Seddah
|
Transfer learning based on pretraining language models on a large amount of raw data has become a new norm to reach state-of-the-art performance in NLP. Still, it remains unclear how this approach should be applied for unseen languages that are not covered by any available large-scale multilingual language model and for which only a small amount of raw data is generally available. In this work, by comparing multilingual and monolingual models, we show that such models behave in multiple ways on unseen languages. Some languages greatly benefit from transfer learning and behave similarly to closely related high resource languages whereas others apparently do not. Focusing on the latter, we show that this failure to transfer is largely related to the impact of the script used to write such languages. We show that transliterating those languages significantly improves the potential of large-scale multilingual language models on downstream tasks. This result provides a promising direction towards making these massively multilingual models useful for a new set of unseen languages.
|
https://aclanthology.org/2021.naacl-main.38
|
https://aclanthology.org/2021.naacl-main.38.pdf
|
NAACL 2021
|
|||
Multi-Adversarial Learning for Cross-Lingual Word Embeddings
|
Haozhou Wang, James Henderson, Paola Merlo
|
Generative adversarial networks (GANs) have succeeded in inducing cross-lingual word embeddings - maps of matching words across languages - without supervision. Despite these successes, GANs’ performance for the difficult case of distant languages is still not satisfactory. These limitations have been explained by GANs’ incorrect assumption that source and target embedding spaces are related by a single linear mapping and are approximately isomorphic. We assume instead that, especially across distant languages, the mapping is only piece-wise linear, and propose a multi-adversarial learning method. This novel method induces the seed cross-lingual dictionary through multiple mappings, each induced to fit the mapping for one subspace. Our experiments on unsupervised bilingual lexicon induction and cross-lingual document classification show that this method improves performance over previous single-mapping methods, especially for distant languages.
|
https://aclanthology.org/2021.naacl-main.39
|
https://aclanthology.org/2021.naacl-main.39.pdf
|
NAACL 2021
|
|||
Multi-view Subword Regularization
|
Xinyi Wang, Sebastian Ruder, Graham Neubig
|
Multilingual pretrained representations generally rely on subword segmentation algorithms to create a shared multilingual vocabulary. However, standard heuristic algorithms often lead to sub-optimal segmentation, especially for languages with limited amounts of data. In this paper, we take two major steps towards alleviating this problem. First, we demonstrate empirically that applying existing subword regularization methods (Kudo, 2018; Provilkov et al., 2020) during fine-tuning of pre-trained multilingual representations improves the effectiveness of cross-lingual transfer. Second, to take full advantage of different possible input segmentations, we propose Multi-view Subword Regularization (MVR), a method that enforces the consistency of predictors between using inputs tokenized by the standard and probabilistic segmentations. Results on the XTREME multilingual benchmark (Hu et al., 2020) show that MVR brings consistent improvements of up to 2.5 points over using standard segmentation algorithms.
|
https://aclanthology.org/2021.naacl-main.40
|
https://aclanthology.org/2021.naacl-main.40.pdf
|
NAACL 2021
|
|||
mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer
|
Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel
|
The recent “Text-to-Text Transfer Transformer” (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks. In this paper, we introduce mT5, a multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages. We detail the design and modified training of mT5 and demonstrate its state-of-the-art performance on many multilingual benchmarks. We also describe a simple technique to prevent “accidental translation” in the zero-shot setting, where a generative model chooses to (partially) translate its prediction into the wrong language. All of the code and model checkpoints used in this work are publicly available.
|
https://aclanthology.org/2021.naacl-main.41
|
https://aclanthology.org/2021.naacl-main.41.pdf
|
NAACL 2021
|
|||
MetaXL: Meta Representation Transformation for Low-resource Cross-lingual Learning
|
Mengzhou Xia, Guoqing Zheng, Subhabrata Mukherjee, Milad Shokouhi, Graham Neubig, Ahmed Hassan Awadallah
|
The combination of multilingual pre-trained representations and cross-lingual transfer learning is one of the most effective methods for building functional NLP systems for low-resource languages. However, for extremely low-resource languages without large-scale monolingual corpora for pre-training or sufficient annotated data for fine-tuning, transfer learning remains an understudied and challenging task. Moreover, recent work shows that multilingual representations are surprisingly disjoint across languages, bringing additional challenges for transfer onto extremely low-resource languages. In this paper, we propose MetaXL, a meta-learning based framework that learns to transform representations judiciously from auxiliary languages to a target one and brings their representation spaces closer for effective transfer. Extensive experiments on real-world low-resource languages – without access to large-scale monolingual corpora or large amounts of labeled data – for tasks like cross-lingual sentiment analysis and named entity recognition show the effectiveness of our approach. Code for MetaXL is publicly available at github.com/microsoft/MetaXL.
|
https://aclanthology.org/2021.naacl-main.42
|
https://aclanthology.org/2021.naacl-main.42.pdf
|
NAACL 2021
|
|||
Open Domain Question Answering over Tables via Dense Retrieval
|
Jonathan Herzig, Thomas Müller, Syrine Krichene, Julian Eisenschlos
|
Recent advances in open-domain QA have led to strong models based on dense retrieval, but only focused on retrieving textual passages. In this work, we tackle open-domain QA over tables for the first time, and show that retrieval can be improved by a retriever designed to handle tabular context. We present an effective pre-training procedure for our retriever and improve retrieval quality with mined hard negatives. As relevant datasets are missing, we extract a subset of Natural Questions (Kwiatkowski et al., 2019) into a Table QA dataset. We find that our retriever improves retrieval results from 72.0 to 81.1 recall@10 and end-to-end QA results from 33.8 to 37.7 exact match, over a BERT based retriever.
|
https://aclanthology.org/2021.naacl-main.43
|
https://aclanthology.org/2021.naacl-main.43.pdf
|
NAACL 2021
|
|||
Open-Domain Question Answering Goes Conversational via Question Rewriting
|
Raviteja Anantha, Svitlana Vakulenko, Zhucheng Tu, Shayne Longpre, Stephen Pulman, Srinivas Chappidi
|
We introduce a new dataset for Question Rewriting in Conversational Context (QReCC), which contains 14K conversations with 80K question-answer pairs. The task in QReCC is to find answers to conversational questions within a collection of 10M web pages (split into 54M passages). Answers to questions in the same conversation may be distributed across several web pages. QReCC provides annotations that allow us to train and evaluate individual subtasks of question rewriting, passage retrieval and reading comprehension required for the end-to-end conversational question answering (QA) task. We report the effectiveness of a strong baseline approach that combines the state-of-the-art model for question rewriting, and competitive models for open-domain QA. Our results set the first baseline for the QReCC dataset with F1 of 19.10, compared to the human upper bound of 75.45, indicating the difficulty of the setup and a large room for improvement.
|
https://aclanthology.org/2021.naacl-main.44
|
https://aclanthology.org/2021.naacl-main.44.pdf
|
NAACL 2021
|
|||
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
|
Michihiro Yasunaga, Hongyu Ren, Antoine Bosselut, Percy Liang, Jure Leskovec
|
The problem of answering questions using knowledge from pre-trained language models (LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question and answer choice), methods need to (i) identify relevant knowledge from large KGs, and (ii) perform joint reasoning over the QA context and KG. Here we propose a new model, QA-GNN, which addresses the above challenges through two key innovations: (i) relevance scoring, where we use LMs to estimate the importance of KG nodes relative to the given QA context, and (ii) joint reasoning, where we connect the QA context and KG to form a joint graph, and mutually update their representations through graph-based message passing. We evaluate QA-GNN on the CommonsenseQA and OpenBookQA datasets, and show its improvement over existing LM and LM+KG models, as well as its capability to perform interpretable and structured reasoning, e.g., correctly handling negation in questions.
|
https://aclanthology.org/2021.naacl-main.45
|
https://aclanthology.org/2021.naacl-main.45.pdf
|
NAACL 2021
|
|||
XOR QA: Cross-lingual Open-Retrieval Question Answering
|
Akari Asai, Jungo Kasai, Jonathan Clark, Kenton Lee, Eunsol Choi, Hannaneh Hajishirzi
|
{'url': 'https://nlp.cs.washington.edu/xorqa/', '#text': 'Multilingual question answering tasks typically assume that answers exist in the same language as the question. Yet in practice, many languages face both information scarcity—where languages have few reference articles—and information asymmetry—where questions reference concepts from other cultures. This work extends open-retrieval question answering to a cross-lingual setting enabling questions from one language to be answered via answer content from another language. We construct a large-scale dataset built on 40K information-seeking questions across 7 diverse non-English languages that TyDi QA could not find same-language answers for. Based on this dataset, we introduce a task framework, called Cross-lingual Open-Retrieval Question Answering (XOR QA), that consists of three new tasks involving cross-lingual document retrieval from multilingual and English resources. We establish baselines with state-of-the-art machine translation systems and cross-lingual pretrained models. Experimental results suggest that XOR QA is a challenging task that will facilitate the development of novel techniques for multilingual question answering. Our data and code are available at .'}
|
https://aclanthology.org/2021.naacl-main.46
|
https://aclanthology.org/2021.naacl-main.46.pdf
|
NAACL 2021
|
|||
SPARTA: Efficient Open-Domain Question Answering via Sparse Transformer Matching Retrieval
|
Tiancheng Zhao, Xiaopeng Lu, Kyusong Lee
|
We introduce SPARTA, a novel neural retrieval method that shows great promise in performance, generalization, and interpretability for open-domain question answering. Unlike many neural ranking methods that use dense vector nearest neighbor search, SPARTA learns a sparse representation that can be efficiently implemented as an Inverted Index. The resulting representation enables scalable neural retrieval that does not require expensive approximate vector search and leads to better performance than its dense counterpart. We validated our approaches on 4 open-domain question answering (OpenQA) tasks and 11 retrieval question answering (ReQA) tasks. SPARTA achieves new state-of-the-art results across a variety of open-domain question answering tasks in both English and Chinese datasets, including open SQuAD, CMRC and etc. Analysis also confirms that the proposed method creates human interpretable representation and allows flexible control over the trade-off between performance and efficiency.
|
https://aclanthology.org/2021.naacl-main.47
|
https://aclanthology.org/2021.naacl-main.47.pdf
|
NAACL 2021
|
|||
Implicitly Abusive Language – What does it actually look like and why are we not getting there?
|
Michael Wiegand, Josef Ruppenhofer, Elisabeth Eder
|
Abusive language detection is an emerging field in natural language processing which has received a large amount of attention recently. Still the success of automatic detection is limited. Particularly, the detection of implicitly abusive language, i.e. abusive language that is not conveyed by abusive words (e.g. dumbass or scum), is not working well. In this position paper, we explain why existing datasets make learning implicit abuse difficult and what needs to be changed in the design of such datasets. Arguing for a divide-and-conquer strategy, we present a list of subtypes of implicitly abusive language and formulate research tasks and questions for future research.
|
https://aclanthology.org/2021.naacl-main.48
|
https://aclanthology.org/2021.naacl-main.48.pdf
|
NAACL 2021
|
|||
The Importance of Modeling Social Factors of Language: Theory and Practice
|
Dirk Hovy, Diyi Yang
|
Natural language processing (NLP) applications are now more powerful and ubiquitous than ever before. With rapidly developing (neural) models and ever-more available data, current NLP models have access to more information than any human speaker during their life. Still, it would be hard to argue that NLP models have reached human-level capacity. In this position paper, we argue that the reason for the current limitations is a focus on information content while ignoring language’s social factors. We show that current NLP systems systematically break down when faced with interpreting the social factors of language. This limits applications to a subset of information-related tasks and prevents NLP from reaching human-level performance. At the same time, systems that incorporate even a minimum of social factors already show remarkable improvements. We formalize a taxonomy of seven social factors based on linguistic theory and exemplify current failures and emerging successes for each of them. We suggest that the NLP community address social factors to get closer to the goal of human-like language understanding.
|
https://aclanthology.org/2021.naacl-main.49
|
https://aclanthology.org/2021.naacl-main.49.pdf
|
NAACL 2021
|
|||
On learning and representing social meaning in NLP: a sociolinguistic perspective
|
Dong Nguyen, Laura Rosseel, Jack Grieve
|
The field of NLP has made substantial progress in building meaning representations. However, an important aspect of linguistic meaning, social meaning, has been largely overlooked. We introduce the concept of social meaning to NLP and discuss how insights from sociolinguistics can inform work on representation learning in NLP. We also identify key challenges for this new line of research.
|
https://aclanthology.org/2021.naacl-main.50
|
https://aclanthology.org/2021.naacl-main.50.pdf
|
NAACL 2021
|
|||
Preregistering NLP research
|
Emiel van Miltenburg, Chris van der Lee, Emiel Krahmer
|
Preregistration refers to the practice of specifying what you are going to do, and what you expect to find in your study, before carrying out the study. This practice is increasingly common in medicine and psychology, but is rarely discussed in NLP. This paper discusses preregistration in more detail, explores how NLP researchers could preregister their work, and presents several preregistration questions for different kinds of studies. Finally, we argue in favour of registered reports, which could provide firmer grounds for slow science in NLP research. The goal of this paper is to elicit a discussion in the NLP community, which we hope to synthesise into a general NLP preregistration form in future research.
|
https://aclanthology.org/2021.naacl-main.51
|
https://aclanthology.org/2021.naacl-main.51.pdf
|
NAACL 2021
|
|||
Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence
|
Tal Schuster, Adam Fisch, Regina Barzilay
|
Typical fact verification models use retrieved written evidence to verify claims. Evidence sources, however, often change over time as more information is gathered and revised. In order to adapt, models must be sensitive to subtle differences in supporting evidence. We present VitaminC, a benchmark infused with challenging cases that require fact verification models to discern and adjust to slight factual changes. We collect over 100,000 Wikipedia revisions that modify an underlying fact, and leverage these revisions, together with additional synthetically constructed ones, to create a total of over 400,000 claim-evidence pairs. Unlike previous resources, the examples in VitaminC are contrastive, i.e., they contain evidence pairs that are nearly identical in language and content, with the exception that one supports a given claim while the other does not. We show that training using this design increases robustness—improving accuracy by 10% on adversarial fact verification and 6% on adversarial natural language inference (NLI). Moreover, the structure of VitaminC leads us to define additional tasks for fact-checking resources: tagging relevant words in the evidence for verifying the claim, identifying factual revisions, and providing automatic edits via factually consistent text generation.
|
https://aclanthology.org/2021.naacl-main.52
|
https://aclanthology.org/2021.naacl-main.52.pdf
|
NAACL 2021
|
|||
Representing Numbers in NLP: a Survey and a Vision
|
Avijit Thawani, Jay Pujara, Filip Ilievski, Pedro Szekely
|
NLP systems rarely give special consideration to numbers found in text. This starkly contrasts with the consensus in neuroscience that, in the brain, numbers are represented differently from words. We arrange recent NLP work on numeracy into a comprehensive taxonomy of tasks and methods. We break down the subjective notion of numeracy into 7 subtasks, arranged along two dimensions: granularity (exact vs approximate) and units (abstract vs grounded). We analyze the myriad representational choices made by over a dozen previously published number encoders and decoders. We synthesize best practices for representing numbers in text and articulate a vision for holistic numeracy in NLP, comprised of design trade-offs and a unified evaluation.
|
https://aclanthology.org/2021.naacl-main.53
|
https://aclanthology.org/2021.naacl-main.53.pdf
|
NAACL 2021
|
|||
Extending Multi-Document Summarization Evaluation to the Interactive Setting
|
Ori Shapira, Ramakanth Pasunuru, Hadar Ronen, Mohit Bansal, Yael Amsterdamer, Ido Dagan
|
Allowing users to interact with multi-document summarizers is a promising direction towards improving and customizing summary results. Different ideas for interactive summarization have been proposed in previous work but these solutions are highly divergent and incomparable. In this paper, we develop an end-to-end evaluation framework for interactive summarization, focusing on expansion-based interaction, which considers the accumulating information along a user session. Our framework includes a procedure of collecting real user sessions, as well as evaluation measures relying on summarization standards, but adapted to reflect interaction. All of our solutions and resources are available publicly as a benchmark, allowing comparison of future developments in interactive summarization, and spurring progress in its methodological evaluation. We demonstrate the use of our framework by evaluating and comparing baseline implementations that we developed for this purpose, which will serve as part of our benchmark. Our extensive experimentation and analysis motivate the proposed evaluation framework design and support its viability.
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https://aclanthology.org/2021.naacl-main.54
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https://aclanthology.org/2021.naacl-main.54.pdf
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NAACL 2021
|
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Identifying Helpful Sentences in Product Reviews
|
Iftah Gamzu, Hila Gonen, Gilad Kutiel, Ran Levy, Eugene Agichtein
|
In recent years online shopping has gained momentum and became an important venue for customers wishing to save time and simplify their shopping process. A key advantage of shopping online is the ability to read what other customers are saying about products of interest. In this work, we aim to maintain this advantage in situations where extreme brevity is needed, for example, when shopping by voice. We suggest a novel task of extracting a single representative helpful sentence from a set of reviews for a given product. The selected sentence should meet two conditions: first, it should be helpful for a purchase decision and second, the opinion it expresses should be supported by multiple reviewers. This task is closely related to the task of Multi Document Summarization in the product reviews domain but differs in its objective and its level of conciseness. We collect a dataset in English of sentence helpfulness scores via crowd-sourcing and demonstrate its reliability despite the inherent subjectivity involved. Next, we describe a complete model that extracts representative helpful sentences with positive and negative sentiment towards the product and demonstrate that it outperforms several baselines.
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https://aclanthology.org/2021.naacl-main.55
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https://aclanthology.org/2021.naacl-main.55.pdf
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NAACL 2021
|
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Noisy Self-Knowledge Distillation for Text Summarization
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Yang Liu, Sheng Shen, Mirella Lapata
|
In this paper we apply self-knowledge distillation to text summarization which we argue can alleviate problems with maximum-likelihood training on single reference and noisy datasets. Instead of relying on one-hot annotation labels, our student summarization model is trained with guidance from a teacher which generates smoothed labels to help regularize training. Furthermore, to better model uncertainty during training, we introduce multiple noise signals for both teacher and student models. We demonstrate experimentally on three benchmarks that our framework boosts the performance of both pretrained and non-pretrained summarizers achieving state-of-the-art results.
|
https://aclanthology.org/2021.naacl-main.56
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https://aclanthology.org/2021.naacl-main.56.pdf
|
NAACL 2021
|
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Improving Zero and Few-Shot Abstractive Summarization with Intermediate Fine-tuning and Data Augmentation
|
Alexander Fabbri, Simeng Han, Haoyuan Li, Haoran Li, Marjan Ghazvininejad, Shafiq Joty, Dragomir Radev, Yashar Mehdad
|
Models pretrained with self-supervised objectives on large text corpora achieve state-of-the-art performance on English text summarization tasks. However, these models are typically fine-tuned on hundreds of thousands of data points, an infeasible requirement when applying summarization to new, niche domains. In this work, we introduce a novel and generalizable method, called WikiTransfer, for fine-tuning pretrained models for summarization in an unsupervised, dataset-specific manner. WikiTransfer fine-tunes pretrained models on pseudo-summaries, produced from generic Wikipedia data, which contain characteristics of the target dataset, such as the length and level of abstraction of the desired summaries. WikiTransfer models achieve state-of-the-art, zero-shot abstractive summarization performance on the CNN-DailyMail dataset and demonstrate the effectiveness of our approach on three additional diverse datasets. These models are more robust to noisy data and also achieve better or comparable few-shot performance using 10 and 100 training examples when compared to few-shot transfer from other summarization datasets. To further boost performance, we employ data augmentation via round-trip translation as well as introduce a regularization term for improved few-shot transfer. To understand the role of dataset aspects in transfer performance and the quality of the resulting output summaries, we further study the effect of the components of our unsupervised fine-tuning data and analyze few-shot performance using both automatic and human evaluation.
|
https://aclanthology.org/2021.naacl-main.57
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https://aclanthology.org/2021.naacl-main.57.pdf
|
NAACL 2021
|
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Enhancing Factual Consistency of Abstractive Summarization
|
Chenguang Zhu, William Hinthorn, Ruochen Xu, Qingkai Zeng, Michael Zeng, Xuedong Huang, Meng Jiang
|
Automatic abstractive summaries are found to often distort or fabricate facts in the article. This inconsistency between summary and original text has seriously impacted its applicability. We propose a fact-aware summarization model FASum to extract and integrate factual relations into the summary generation process via graph attention. We then design a factual corrector model FC to automatically correct factual errors from summaries generated by existing systems. Empirical results show that the fact-aware summarization can produce abstractive summaries with higher factual consistency compared with existing systems, and the correction model improves the factual consistency of given summaries via modifying only a few keywords.
|
https://aclanthology.org/2021.naacl-main.58
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https://aclanthology.org/2021.naacl-main.58.pdf
|
NAACL 2021
|
|||
Few-shot Intent Classification and Slot Filling with Retrieved Examples
|
Dian Yu, Luheng He, Yuan Zhang, Xinya Du, Panupong Pasupat, Qi Li
|
Few-shot learning arises in important practical scenarios, such as when a natural language understanding system needs to learn new semantic labels for an emerging, resource-scarce domain. In this paper, we explore retrieval-based methods for intent classification and slot filling tasks in few-shot settings. Retrieval-based methods make predictions based on labeled examples in the retrieval index that are similar to the input, and thus can adapt to new domains simply by changing the index without having to retrain the model. However, it is non-trivial to apply such methods on tasks with a complex label space like slot filling. To this end, we propose a span-level retrieval method that learns similar contextualized representations for spans with the same label via a novel batch-softmax objective. At inference time, we use the labels of the retrieved spans to construct the final structure with the highest aggregated score. Our method outperforms previous systems in various few-shot settings on the CLINC and SNIPS benchmarks.
|
https://aclanthology.org/2021.naacl-main.59
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https://aclanthology.org/2021.naacl-main.59.pdf
|
NAACL 2021
|
|||
“Nice Try, Kiddo”: Investigating Ad Hominems in Dialogue Responses
|
Emily Sheng, Kai-Wei Chang, Prem Natarajan, Nanyun Peng
|
Ad hominem attacks are those that target some feature of a person’s character instead of the position the person is maintaining. These attacks are harmful because they propagate implicit biases and diminish a person’s credibility. Since dialogue systems respond directly to user input, it is important to study ad hominems in dialogue responses. To this end, we propose categories of ad hominems, compose an annotated dataset, and build a classifier to analyze human and dialogue system responses to English Twitter posts. We specifically compare responses to Twitter topics about marginalized communities (#BlackLivesMatter, #MeToo) versus other topics (#Vegan, #WFH), because the abusive language of ad hominems could further amplify the skew of power away from marginalized populations. Furthermore, we propose a constrained decoding technique that uses salient n-gram similarity as a soft constraint for top-k sampling to reduce the amount of ad hominems generated. Our results indicate that 1) responses from both humans and DialoGPT contain more ad hominems for discussions around marginalized communities, 2) different quantities of ad hominems in the training data can influence the likelihood of generating ad hominems, and 3) we can use constrained decoding techniques to reduce ad hominems in generated dialogue responses.
|
https://aclanthology.org/2021.naacl-main.60
|
https://aclanthology.org/2021.naacl-main.60.pdf
|
NAACL 2021
|
|||
Human-like informative conversations: Better acknowledgements using conditional mutual information
|
Ashwin Paranjape, Christopher Manning
|
This work aims to build a dialogue agent that can weave new factual content into conversations as naturally as humans. We draw insights from linguistic principles of conversational analysis and annotate human-human conversations from the Switchboard Dialog Act Corpus to examine humans strategies for acknowledgement, transition, detail selection and presentation. When current chatbots (explicitly provided with new factual content) introduce facts into a conversation, their generated responses do not acknowledge the prior turns. This is because models trained with two contexts - new factual content and conversational history - generate responses that are non-specific w.r.t. one of the contexts, typically the conversational history. We show that specificity w.r.t. conversational history is better captured by pointwise conditional mutual information (pcmi_h) than by the established use of pointwise mutual information (pmi). Our proposed method, Fused-PCMI, trades off pmi for pcmi_h and is preferred by humans for overall quality over the Max-PMI baseline 60% of the time. Human evaluators also judge responses with higher pcmi_h better at acknowledgement 74% of the time. The results demonstrate that systems mimicking human conversational traits (in this case acknowledgement) improve overall quality and more broadly illustrate the utility of linguistic principles in improving dialogue agents.
|
https://aclanthology.org/2021.naacl-main.61
|
https://aclanthology.org/2021.naacl-main.61.pdf
|
NAACL 2021
|
|||
A Comparative Study on Schema-Guided Dialogue State Tracking
|
Jie Cao, Yi Zhang
|
Frame-based state representation is widely used in modern task-oriented dialog systems to model user intentions and slot values. However, a fixed design of domain ontology makes it difficult to extend to new services and APIs. Recent work proposed to use natural language descriptions to define the domain ontology instead of tag names for each intent or slot, thus offering a dynamic set of schema. In this paper, we conduct in-depth comparative studies to understand the use of natural language description for schema in dialog state tracking. Our discussion mainly covers three aspects: encoder architectures, impact of supplementary training, and effective schema description styles. We introduce a set of newly designed bench-marking descriptions and reveal the model robustness on both homogeneous and heterogeneous description styles in training and evaluation.
|
https://aclanthology.org/2021.naacl-main.62
|
https://aclanthology.org/2021.naacl-main.62.pdf
|
NAACL 2021
|
|||
Spoken Language Understanding for Task-oriented Dialogue Systems with Augmented Memory Networks
|
Jie Wu, Ian Harris, Hongzhi Zhao
|
Spoken language understanding, usually including intent detection and slot filling, is a core component to build a spoken dialog system. Recent research shows promising results by jointly learning of those two tasks based on the fact that slot filling and intent detection are sharing semantic knowledge. Furthermore, attention mechanism boosts joint learning to achieve state-of-the-art results. However, current joint learning models ignore the following important facts: 1. Long-term slot context is not traced effectively, which is crucial for future slot filling. 2. Slot tagging and intent detection could be mutually rewarding, but bi-directional interaction between slot filling and intent detection remains seldom explored. In this paper, we propose a novel approach to model long-term slot context and to fully utilize the semantic correlation between slots and intents. We adopt a key-value memory network to model slot context dynamically and to track more important slot tags decoded before, which are then fed into our decoder for slot tagging. Furthermore, gated memory information is utilized to perform intent detection, mutually improving both tasks through global optimization. Experiments on benchmark ATIS and Snips datasets show that our model achieves state-of-the-art performance and outperforms other methods, especially for the slot filling task.
|
https://aclanthology.org/2021.naacl-main.63
|
https://aclanthology.org/2021.naacl-main.63.pdf
|
NAACL 2021
|
|||
How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds
|
Prithviraj Ammanabrolu, Jack Urbanek, Margaret Li, Arthur Szlam, Tim Rocktäschel, Jason Weston
|
We seek to create agents that both act and communicate with other agents in pursuit of a goal. Towards this end, we extend LIGHT (Urbanek et al. 2019)—a large-scale crowd-sourced fantasy text-game—with a dataset of quests. These contain natural language motivations paired with in-game goals and human demonstrations; completing a quest might require dialogue or actions (or both). We introduce a reinforcement learning system that (1) incorporates large-scale language modeling-based and commonsense reasoning-based pre-training to imbue the agent with relevant priors; and (2) leverages a factorized action space of action commands and dialogue, balancing between the two. We conduct zero-shot evaluations using held-out human expert demonstrations, showing that our agents are able to act consistently and talk naturally with respect to their motivations.
|
https://aclanthology.org/2021.naacl-main.64
|
https://aclanthology.org/2021.naacl-main.64.pdf
|
NAACL 2021
|
|||
Linking Entities to Unseen Knowledge Bases with Arbitrary Schemas
|
Yogarshi Vyas, Miguel Ballesteros
|
In entity linking, mentions of named entities in raw text are disambiguated against a knowledge base (KB). This work focuses on linking to unseen KBs that do not have training data and whose schema is unknown during training. Our approach relies on methods to flexibly convert entities with several attribute-value pairs from arbitrary KBs into flat strings, which we use in conjunction with state-of-the-art models for zero-shot linking. We further improve the generalization of our model using two regularization schemes based on shuffling of entity attributes and handling of unseen attributes. Experiments on English datasets where models are trained on the CoNLL dataset, and tested on the TAC-KBP 2010 dataset show that our models are 12% (absolute) more accurate than baseline models that simply flatten entities from the target KB. Unlike prior work, our approach also allows for seamlessly combining multiple training datasets. We test this ability by adding both a completely different dataset (Wikia), as well as increasing amount of training data from the TAC-KBP 2010 training set. Our models are more accurate across the board compared to baselines.
|
https://aclanthology.org/2021.naacl-main.65
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https://aclanthology.org/2021.naacl-main.65.pdf
|
NAACL 2021
|
|||
Self-Training with Weak Supervision
|
Giannis Karamanolakis, Subhabrata Mukherjee, Guoqing Zheng, Ahmed Hassan Awadallah
|
State-of-the-art deep neural networks require large-scale labeled training data that is often expensive to obtain or not available for many tasks. Weak supervision in the form of domain-specific rules has been shown to be useful in such settings to automatically generate weakly labeled training data. However, learning with weak rules is challenging due to their inherent heuristic and noisy nature. An additional challenge is rule coverage and overlap, where prior work on weak supervision only considers instances that are covered by weak rules, thus leaving valuable unlabeled data behind. In this work, we develop a weak supervision framework (ASTRA) that leverages all the available data for a given task. To this end, we leverage task-specific unlabeled data through self-training with a model (student) that considers contextualized representations and predicts pseudo-labels for instances that may not be covered by weak rules. We further develop a rule attention network (teacher) that learns how to aggregate student pseudo-labels with weak rule labels, conditioned on their fidelity and the underlying context of an instance. Finally, we construct a semi-supervised learning objective for end-to-end training with unlabeled data, domain-specific rules, and a small amount of labeled data. Extensive experiments on six benchmark datasets for text classification demonstrate the effectiveness of our approach with significant improvements over state-of-the-art baselines.
|
https://aclanthology.org/2021.naacl-main.66
|
https://aclanthology.org/2021.naacl-main.66.pdf
|
NAACL 2021
|
|||
Neural Language Modeling for Contextualized Temporal Graph Generation
|
Aman Madaan, Yiming Yang
|
This paper presents the first study on using large-scale pre-trained language models for automated generation of an event-level temporal graph for a document. Despite the huge success of neural pre-training methods in NLP tasks, its potential for temporal reasoning over event graphs has not been sufficiently explored. Part of the reason is the difficulty in obtaining large training corpora with human-annotated events and temporal links. We address this challenge by using existing IE/NLP tools to automatically generate a large quantity (89,000) of system-produced document-graph pairs, and propose a novel formulation of the contextualized graph generation problem as a sequence-to-sequence mapping task. These strategies enable us to leverage and fine-tune pre-trained language models on the system-induced training data for the graph generation task. Our experiments show that our approach is highly effective in generating structurally and semantically valid graphs. Further, evaluation on a challenging hand-labeled, out-of-domain corpus shows that our method outperforms the closest existing method by a large margin on several metrics. We also show a downstream application of our approach by adapting it to answer open-ended temporal questions in a reading comprehension setting.
|
https://aclanthology.org/2021.naacl-main.67
|
https://aclanthology.org/2021.naacl-main.67.pdf
|
NAACL 2021
|
|||
Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning
|
Xuelu Chen, Michael Boratko, Muhao Chen, Shib Sankar Dasgupta, Xiang Lorraine Li, Andrew McCallum
|
Knowledge bases often consist of facts which are harvested from a variety of sources, many of which are noisy and some of which conflict, resulting in a level of uncertainty for each triple. Knowledge bases are also often incomplete, prompting the use of embedding methods to generalize from known facts, however, existing embedding methods only model triple-level uncertainty, and reasoning results lack global consistency. To address these shortcomings, we propose BEUrRE, a novel uncertain knowledge graph embedding method with calibrated probabilistic semantics. BEUrRE models each entity as a box (i.e. axis-aligned hyperrectangle) and relations between two entities as affine transforms on the head and tail entity boxes. The geometry of the boxes allows for efficient calculation of intersections and volumes, endowing the model with calibrated probabilistic semantics and facilitating the incorporation of relational constraints. Extensive experiments on two benchmark datasets show that BEUrRE consistently outperforms baselines on confidence prediction and fact ranking due to its probabilistic calibration and ability to capture high-order dependencies among facts.
|
https://aclanthology.org/2021.naacl-main.68
|
https://aclanthology.org/2021.naacl-main.68.pdf
|
NAACL 2021
|
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Document-Level Event Argument Extraction by Conditional Generation
|
Sha Li, Heng Ji, Jiawei Han
|
Event extraction has long been treated as a sentence-level task in the IE community. We argue that this setting does not match human informative seeking behavior and leads to incomplete and uninformative extraction results. We propose a document-level neural event argument extraction model by formulating the task as conditional generation following event templates. We also compile a new document-level event extraction benchmark dataset WikiEvents which includes complete event and coreference annotation. On the task of argument extraction, we achieve an absolute gain of 7.6% F1 and 5.7% F1 over the next best model on the RAMS and WikiEvents dataset respectively. On the more challenging task of informative argument extraction, which requires implicit coreference reasoning, we achieve a 9.3% F1 gain over the best baseline. To demonstrate the portability of our model, we also create the first end-to-end zero-shot event extraction framework and achieve 97% of fully supervised model’s trigger extraction performance and 82% of the argument extraction performance given only access to 10 out of the 33 types on ACE.
|
https://aclanthology.org/2021.naacl-main.69
|
https://aclanthology.org/2021.naacl-main.69.pdf
|
NAACL 2021
|
|||
Template Filling with Generative Transformers
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Xinya Du, Alexander Rush, Claire Cardie
|
Template filling is generally tackled by a pipeline of two separate supervised systems – one for role-filler extraction and another for template/event recognition. Since pipelines consider events in isolation, they can suffer from error propagation. We introduce a framework based on end-to-end generative transformers for this task (i.e., GTT). It naturally models the dependence between entities both within a single event and across the multiple events described in a document. Experiments demonstrate that this framework substantially outperforms pipeline-based approaches, and other neural end-to-end baselines that do not model between-event dependencies. We further show that our framework specifically improves performance on documents containing multiple events.
|
https://aclanthology.org/2021.naacl-main.70
|
https://aclanthology.org/2021.naacl-main.70.pdf
|
NAACL 2021
|
|||
Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU models
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Mengnan Du, Varun Manjunatha, Rajiv Jain, Ruchi Deshpande, Franck Dernoncourt, Jiuxiang Gu, Tong Sun, Xia Hu
|
Recent studies indicate that NLU models are prone to rely on shortcut features for prediction, without achieving true language understanding. As a result, these models fail to generalize to real-world out-of-distribution data. In this work, we show that the words in the NLU training set can be modeled as a long-tailed distribution. There are two findings: 1) NLU models have strong preference for features located at the head of the long-tailed distribution, and 2) Shortcut features are picked up during very early few iterations of the model training. These two observations are further employed to formulate a measurement which can quantify the shortcut degree of each training sample. Based on this shortcut measurement, we propose a shortcut mitigation framework LGTR, to suppress the model from making overconfident predictions for samples with large shortcut degree. Experimental results on three NLU benchmarks demonstrate that our long-tailed distribution explanation accurately reflects the shortcut learning behavior of NLU models. Experimental analysis further indicates that LGTR can improve the generalization accuracy on OOD data, while preserving the accuracy on in-distribution data.
|
https://aclanthology.org/2021.naacl-main.71
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https://aclanthology.org/2021.naacl-main.71.pdf
|
NAACL 2021
|
|||
On Attention Redundancy: A Comprehensive Study
|
Yuchen Bian, Jiaji Huang, Xingyu Cai, Jiahong Yuan, Kenneth Church
|
Multi-layer multi-head self-attention mechanism is widely applied in modern neural language models. Attention redundancy has been observed among attention heads but has not been deeply studied in the literature. Using BERT-base model as an example, this paper provides a comprehensive study on attention redundancy which is helpful for model interpretation and model compression. We analyze the attention redundancy with Five-Ws and How. (What) We define and focus the study on redundancy matrices generated from pre-trained and fine-tuned BERT-base model for GLUE datasets. (How) We use both token-based and sentence-based distance functions to measure the redundancy. (Where) Clear and similar redundancy patterns (cluster structure) are observed among attention heads. (When) Redundancy patterns are similar in both pre-training and fine-tuning phases. (Who) We discover that redundancy patterns are task-agnostic. Similar redundancy patterns even exist for randomly generated token sequences. (“Why”) We also evaluate influences of the pre-training dropout ratios on attention redundancy. Based on the phase-independent and task-agnostic attention redundancy patterns, we propose a simple zero-shot pruning method as a case study. Experiments on fine-tuning GLUE tasks verify its effectiveness. The comprehensive analyses on attention redundancy make model understanding and zero-shot model pruning promising.
|
https://aclanthology.org/2021.naacl-main.72
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https://aclanthology.org/2021.naacl-main.72.pdf
|
NAACL 2021
|
|||
Does BERT Pretrained on Clinical Notes Reveal Sensitive Data?
|
Eric Lehman, Sarthak Jain, Karl Pichotta, Yoav Goldberg, Byron Wallace
|
{'url': 'https://github.com/elehman16/exposing_patient_data_release', '#text': 'Large Transformers pretrained over clinical notes from Electronic Health Records (EHR) have afforded substantial gains in performance on predictive clinical tasks. The cost of training such models (and the necessity of data access to do so) coupled with their utility motivates parameter sharing, i.e., the release of pretrained models such as ClinicalBERT. While most efforts have used deidentified EHR, many researchers have access to large sets of sensitive, non-deidentified EHR with which they might train a BERT model (or similar). Would it be safe to release the weights of such a model if they did? In this work, we design a battery of approaches intended to recover Personal Health Information (PHI) from a trained BERT. Specifically, we attempt to recover patient names and conditions with which they are associated. We find that simple probing methods are not able to meaningfully extract sensitive information from BERT trained over the MIMIC-III corpus of EHR. However, more sophisticated “attacks” may succeed in doing so: To facilitate such research, we make our experimental setup and baseline probing models available at .'}
|
https://aclanthology.org/2021.naacl-main.73
|
https://aclanthology.org/2021.naacl-main.73.pdf
|
NAACL 2021
|
|||
Low-Complexity Probing via Finding Subnetworks
|
Steven Cao, Victor Sanh, Alexander Rush
|
The dominant approach in probing neural networks for linguistic properties is to train a new shallow multi-layer perceptron (MLP) on top of the model’s internal representations. This approach can detect properties encoded in the model, but at the cost of adding new parameters that may learn the task directly. We instead propose a subtractive pruning-based probe, where we find an existing subnetwork that performs the linguistic task of interest. Compared to an MLP, the subnetwork probe achieves both higher accuracy on pre-trained models and lower accuracy on random models, so it is both better at finding properties of interest and worse at learning on its own. Next, by varying the complexity of each probe, we show that subnetwork probing Pareto-dominates MLP probing in that it achieves higher accuracy given any budget of probe complexity. Finally, we analyze the resulting subnetworks across various tasks to locate where each task is encoded, and we find that lower-level tasks are captured in lower layers, reproducing similar findings in past work.
|
https://aclanthology.org/2021.naacl-main.74
|
https://aclanthology.org/2021.naacl-main.74.pdf
|
NAACL 2021
|
|||
An Empirical Comparison of Instance Attribution Methods for NLP
|
Pouya Pezeshkpour, Sarthak Jain, Byron Wallace, Sameer Singh
|
{'url': 'https://github.com/successar/instance_attributions_NLP', '#text': 'Widespread adoption of deep models has motivated a pressing need for approaches to interpret network outputs and to facilitate model debugging. Instance attribution methods constitute one means of accomplishing these goals by retrieving training instances that (may have) led to a particular prediction. Influence functions (IF; Koh and Liang 2017) provide machinery for doing this by quantifying the effect that perturbing individual train instances would have on a specific test prediction. However, even approximating the IF is computationally expensive, to the degree that may be prohibitive in many cases. Might simpler approaches (e.g., retrieving train examples most similar to a given test point) perform comparably? In this work, we evaluate the degree to which different potential instance attribution agree with respect to the importance of training samples. We find that simple retrieval methods yield training instances that differ from those identified via gradient-based methods (such as IFs), but that nonetheless exhibit desirable characteristics similar to more complex attribution methods. Code for all methods and experiments in this paper is available at: .'}
|
https://aclanthology.org/2021.naacl-main.75
|
https://aclanthology.org/2021.naacl-main.75.pdf
|
NAACL 2021
|
|||
Generalization in Instruction Following Systems
|
Soham Dan, Michael Zhou, Dan Roth
|
Understanding and executing natural language instructions in a grounded domain is one of the hallmarks of artificial intelligence. In this paper, we focus on instruction understanding in the blocks world domain and investigate the language understanding abilities of two top-performing systems for the task. We aim to understand if the test performance of these models indicates an understanding of the spatial domain and of the natural language instructions relative to it, or whether they merely over-fit spurious signals in the dataset. We formulate a set of expectations one might have from an instruction following model and concretely characterize the different dimensions of robustness such a model should possess. Despite decent test performance, we find that state-of-the-art models fall short of these expectations and are extremely brittle. We then propose a learning strategy that involves data augmentation and show through extensive experiments that the proposed learning strategy yields models that are competitive on the original test set while satisfying our expectations much better.
|
https://aclanthology.org/2021.naacl-main.76
|
https://aclanthology.org/2021.naacl-main.76.pdf
|
NAACL 2021
|
|||
LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval
|
Siqi Sun, Yen-Chun Chen, Linjie Li, Shuohang Wang, Yuwei Fang, Jingjing Liu
|
Multimodal pre-training has propelled great advancement in vision-and-language research. These large-scale pre-trained models, although successful, fatefully suffer from slow inference speed due to enormous computational cost mainly from cross-modal attention in Transformer architecture. When applied to real-life applications, such latency and computation demand severely deter the practical use of pre-trained models. In this paper, we study Image-text retrieval (ITR), the most mature scenario of V+L application, which has been widely studied even prior to the emergence of recent pre-trained models. We propose a simple yet highly effective approach, LightningDOT that accelerates the inference time of ITR by thousands of times, without sacrificing accuracy. LightningDOT removes the time-consuming cross-modal attention by extracting pre-cached feature indexes offline, and employing instant dot-product matching online, which significantly speeds up retrieval process. In fact, our LightningDOT achieves superior performance across mainstream ITR benchmarks such as Flickr30k and COCO datasets, outperforming existing pre-trained models that consume 1000 times magnitude of computational hours using the same features.
|
https://aclanthology.org/2021.naacl-main.77
|
https://aclanthology.org/2021.naacl-main.77.pdf
|
NAACL 2021
|
|||
Measuring Social Biases in Grounded Vision and Language Embeddings
|
Candace Ross, Boris Katz, Andrei Barbu
|
We generalize the notion of measuring social biases in word embeddings to visually grounded word embeddings. Biases are present in grounded embeddings, and indeed seem to be equally or more significant than for ungrounded embeddings. This is despite the fact that vision and language can suffer from different biases, which one might hope could attenuate the biases in both. Multiple ways exist to generalize metrics measuring bias in word embeddings to this new setting. We introduce the space of generalizations (Grounded-WEAT and Grounded-SEAT) and demonstrate that three generalizations answer different yet important questions about how biases, language, and vision interact. These metrics are used on a new dataset, the first for grounded bias, created by augmenting standard linguistic bias benchmarks with 10,228 images from COCO, Conceptual Captions, and Google Images. Dataset construction is challenging because vision datasets are themselves very biased. The presence of these biases in systems will begin to have real-world consequences as they are deployed, making carefully measuring bias and then mitigating it critical to building a fair society.
|
https://aclanthology.org/2021.naacl-main.78
|
https://aclanthology.org/2021.naacl-main.78.pdf
|
NAACL 2021
|
|||
MTAG: Modal-Temporal Attention Graph for Unaligned Human Multimodal Language Sequences
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Jianing Yang, Yongxin Wang, Ruitao Yi, Yuying Zhu, Azaan Rehman, Amir Zadeh, Soujanya Poria, Louis-Philippe Morency
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Human communication is multimodal in nature; it is through multiple modalities such as language, voice, and facial expressions, that opinions and emotions are expressed. Data in this domain exhibits complex multi-relational and temporal interactions. Learning from this data is a fundamentally challenging research problem. In this paper, we propose Modal-Temporal Attention Graph (MTAG). MTAG is an interpretable graph-based neural model that provides a suitable framework for analyzing multimodal sequential data. We first introduce a procedure to convert unaligned multimodal sequence data into a graph with heterogeneous nodes and edges that captures the rich interactions across modalities and through time. Then, a novel graph fusion operation, called MTAG fusion, along with a dynamic pruning and read-out technique, is designed to efficiently process this modal-temporal graph and capture various interactions. By learning to focus only on the important interactions within the graph, MTAG achieves state-of-the-art performance on multimodal sentiment analysis and emotion recognition benchmarks, while utilizing significantly fewer model parameters.
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https://aclanthology.org/2021.naacl-main.79
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https://aclanthology.org/2021.naacl-main.79.pdf
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NAACL 2021
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Grounding Open-Domain Instructions to Automate Web Support Tasks
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Nancy Xu, Sam Masling, Michael Du, Giovanni Campagna, Larry Heck, James Landay, Monica Lam
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Grounding natural language instructions on the web to perform previously unseen tasks enables accessibility and automation. We introduce a task and dataset to train AI agents from open-domain, step-by-step instructions originally written for people. We build RUSS (Rapid Universal Support Service) to tackle this problem. RUSS consists of two models: First, a BERT-LSTM with pointers parses instructions to WebLang, a domain-specific language we design for grounding natural language on the web. Then, a grounding model retrieves the unique IDs of any webpage elements requested in the WebLang. RUSS may interact with the user through a dialogue (e.g. ask for an address) or execute a web operation (e.g. click a button) inside the web runtime. To augment training, we synthesize natural language instructions mapped to WebLang. Our dataset consists of 80 different customer service problems from help websites, with a total of 741 step-by-step instructions and their corresponding actions. RUSS achieves 76.7% end-to-end accuracy predicting agent actions from single instructions. It outperforms state-of-the-art models that directly map instructions to actions without WebLang. Our user study shows that RUSS is preferred by actual users over web navigation.
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https://aclanthology.org/2021.naacl-main.80
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https://aclanthology.org/2021.naacl-main.80.pdf
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NAACL 2021
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Modular Networks for Compositional Instruction Following
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Rodolfo Corona, Daniel Fried, Coline Devin, Dan Klein, Trevor Darrell
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Standard architectures used in instruction following often struggle on novel compositions of subgoals (e.g. navigating to landmarks or picking up objects) observed during training. We propose a modular architecture for following natural language instructions that describe sequences of diverse subgoals. In our approach, subgoal modules each carry out natural language instructions for a specific subgoal type. A sequence of modules to execute is chosen by learning to segment the instructions and predicting a subgoal type for each segment. When compared to standard, non-modular sequence-to-sequence approaches on ALFRED, a challenging instruction following benchmark, we find that modularization improves generalization to novel subgoal compositions, as well as to environments unseen in training.
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https://aclanthology.org/2021.naacl-main.81
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https://aclanthology.org/2021.naacl-main.81.pdf
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NAACL 2021
|
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Improving Cross-Modal Alignment in Vision Language Navigation via Syntactic Information
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Jialu Li, Hao Tan, Mohit Bansal
|
Vision language navigation is the task that requires an agent to navigate through a 3D environment based on natural language instructions. One key challenge in this task is to ground instructions with the current visual information that the agent perceives. Most of the existing work employs soft attention over individual words to locate the instruction required for the next action. However, different words have different functions in a sentence (e.g., modifiers convey attributes, verbs convey actions). Syntax information like dependencies and phrase structures can aid the agent to locate important parts of the instruction. Hence, in this paper, we propose a navigation agent that utilizes syntax information derived from a dependency tree to enhance alignment between the instruction and the current visual scenes. Empirically, our agent outperforms the baseline model that does not use syntax information on the Room-to-Room dataset, especially in the unseen environment. Besides, our agent achieves the new state-of-the-art on Room-Across-Room dataset, which contains instructions in 3 languages (English, Hindi, and Telugu). We also show that our agent is better at aligning instructions with the current visual information via qualitative visualizations.
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https://aclanthology.org/2021.naacl-main.82
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https://aclanthology.org/2021.naacl-main.82.pdf
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NAACL 2021
|
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Improving Pretrained Models for Zero-shot Multi-label Text Classification through Reinforced Label Hierarchy Reasoning
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Hui Liu, Danqing Zhang, Bing Yin, Xiaodan Zhu
|
Exploiting label hierarchies has become a promising approach to tackling the zero-shot multi-label text classification (ZS-MTC) problem. Conventional methods aim to learn a matching model between text and labels, using a graph encoder to incorporate label hierarchies to obtain effective label representations (Rios and Kavuluru, 2018). More recently, pretrained models like BERT (Devlin et al., 2018) have been used to convert classification tasks into a textual entailment task (Yin et al., 2019). This approach is naturally suitable for the ZS-MTC task. However, pretrained models are underexplored in the existing work because they do not generate individual vector representations for text or labels, making it unintuitive to combine them with conventional graph encoding methods. In this paper, we explore to improve pretrained models with label hierarchies on the ZS-MTC task. We propose a Reinforced Label Hierarchy Reasoning (RLHR) approach to encourage interdependence among labels in the hierarchies during training. Meanwhile, to overcome the weakness of flat predictions, we design a rollback algorithm that can remove logical errors from predictions during inference. Experimental results on three real-life datasets show that our approach achieves better performance and outperforms previous non-pretrained methods on the ZS-MTC task.
|
https://aclanthology.org/2021.naacl-main.83
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https://aclanthology.org/2021.naacl-main.83.pdf
|
NAACL 2021
|
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Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach
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Yue Yu, Simiao Zuo, Haoming Jiang, Wendi Ren, Tuo Zhao, Chao Zhang
|
{'url': 'https://github.com/yueyu1030/COSINE', '#text': 'Fine-tuned pre-trained language models (LMs) have achieved enormous success in many natural language processing (NLP) tasks, but they still require excessive labeled data in the fine-tuning stage. We study the problem of fine-tuning pre-trained LMs using only weak supervision, without any labeled data. This problem is challenging because the high capacity of LMs makes them prone to overfitting the noisy labels generated by weak supervision. To address this problem, we develop a contrastive self-training framework, COSINE, to enable fine-tuning LMs with weak supervision. Underpinned by contrastive regularization and confidence-based reweighting, our framework gradually improves model fitting while effectively suppressing error propagation. Experiments on sequence, token, and sentence pair classification tasks show that our model outperforms the strongest baseline by large margins and achieves competitive performance with fully-supervised fine-tuning methods. Our implementation is available on .'}
|
https://aclanthology.org/2021.naacl-main.84
|
https://aclanthology.org/2021.naacl-main.84.pdf
|
NAACL 2021
|
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Posterior Differential Regularization with f-divergence for Improving Model Robustness
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Hao Cheng, Xiaodong Liu, Lis Pereira, Yaoliang Yu, Jianfeng Gao
|
We address the problem of enhancing model robustness through regularization. Specifically, we focus on methods that regularize the model posterior difference between clean and noisy inputs. Theoretically, we provide a connection of two recent methods, Jacobian Regularization and Virtual Adversarial Training, under this framework. Additionally, we generalize the posterior differential regularization to the family of f-divergences and characterize the overall framework in terms of the Jacobian matrix. Empirically, we compare those regularizations and standard BERT training on a diverse set of tasks to provide a comprehensive profile of their effect on model generalization. For both fully supervised and semi-supervised settings, we show that regularizing the posterior difference with f-divergence can result in well-improved model robustness. In particular, with a proper f-divergence, a BERT-base model can achieve comparable generalization as its BERT-large counterpart for in-domain, adversarial and domain shift scenarios, indicating the great potential of the proposed framework for enhancing NLP model robustness.
|
https://aclanthology.org/2021.naacl-main.85
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https://aclanthology.org/2021.naacl-main.85.pdf
|
NAACL 2021
|
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Understanding Hard Negatives in Noise Contrastive Estimation
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Wenzheng Zhang, Karl Stratos
|
The choice of negative examples is important in noise contrastive estimation. Recent works find that hard negatives—highest-scoring incorrect examples under the model—are effective in practice, but they are used without a formal justification. We develop analytical tools to understand the role of hard negatives. Specifically, we view the contrastive loss as a biased estimator of the gradient of the cross-entropy loss, and show both theoretically and empirically that setting the negative distribution to be the model distribution results in bias reduction. We also derive a general form of the score function that unifies various architectures used in text retrieval. By combining hard negatives with appropriate score functions, we obtain strong results on the challenging task of zero-shot entity linking.
|
https://aclanthology.org/2021.naacl-main.86
|
https://aclanthology.org/2021.naacl-main.86.pdf
|
NAACL 2021
|
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Certified Robustness to Word Substitution Attack with Differential Privacy
|
Wenjie Wang, Pengfei Tang, Jian Lou, Li Xiong
|
The robustness and security of natural language processing (NLP) models are significantly important in real-world applications. In the context of text classification tasks, adversarial examples can be designed by substituting words with synonyms under certain semantic and syntactic constraints, such that a well-trained model will give a wrong prediction. Therefore, it is crucial to develop techniques to provide a rigorous and provable robustness guarantee against such attacks. In this paper, we propose WordDP to achieve certified robustness against word substitution at- tacks in text classification via differential privacy (DP). We establish the connection between DP and adversarial robustness for the first time in the text domain and propose a conceptual exponential mechanism-based algorithm to formally achieve the robustness. We further present a practical simulated exponential mechanism that has efficient inference with certified robustness. We not only provide a rigorous analytic derivation of the certified condition but also experimentally compare the utility of WordDP with existing defense algorithms. The results show that WordDP achieves higher accuracy and more than 30X efficiency improvement over the state-of-the-art certified robustness mechanism in typical text classification tasks.
|
https://aclanthology.org/2021.naacl-main.87
|
https://aclanthology.org/2021.naacl-main.87.pdf
|
NAACL 2021
|
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DReCa: A General Task Augmentation Strategy for Few-Shot Natural Language Inference
|
Shikhar Murty, Tatsunori B. Hashimoto, Christopher Manning
|
Meta-learning promises few-shot learners that can adapt to new distributions by repurposing knowledge acquired from previous training. However, we believe meta-learning has not yet succeeded in NLP due to the lack of a well-defined task distribution, leading to attempts that treat datasets as tasks. Such an ad hoc task distribution causes problems of quantity and quality. Since there’s only a handful of datasets for any NLP problem, meta-learners tend to overfit their adaptation mechanism and, since NLP datasets are highly heterogeneous, many learning episodes have poor transfer between their support and query sets, which discourages the meta-learner from adapting. To alleviate these issues, we propose DReCA (Decomposing datasets into Reasoning Categories), a simple method for discovering and using latent reasoning categories in a dataset, to form additional high quality tasks. DReCA works by splitting examples into label groups, embedding them with a finetuned BERT model and then clustering each group into reasoning categories. Across four few-shot NLI problems, we demonstrate that using DReCA improves the accuracy of meta-learners by 1.5-4%
|
https://aclanthology.org/2021.naacl-main.88
|
https://aclanthology.org/2021.naacl-main.88.pdf
|
NAACL 2021
|
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Harnessing Multilinguality in Unsupervised Machine Translation for Rare Languages
|
Xavier Garcia, Aditya Siddhant, Orhan Firat, Ankur Parikh
|
Unsupervised translation has reached impressive performance on resource-rich language pairs such as English-French and English-German. However, early studies have shown that in more realistic settings involving low-resource, rare languages, unsupervised translation performs poorly, achieving less than 3.0 BLEU. In this work, we show that multilinguality is critical to making unsupervised systems practical for low-resource settings. In particular, we present a single model for 5 low-resource languages (Gujarati, Kazakh, Nepali, Sinhala, and Turkish) to and from English directions, which leverages monolingual and auxiliary parallel data from other high-resource language pairs via a three-stage training scheme. We outperform all current state-of-the-art unsupervised baselines for these languages, achieving gains of up to 14.4 BLEU. Additionally, we outperform strong supervised baselines for various language pairs as well as match the performance of the current state-of-the-art supervised model for Nepali-English. We conduct a series of ablation studies to establish the robustness of our model under different degrees of data quality, as well as to analyze the factors which led to the superior performance of the proposed approach over traditional unsupervised models.
|
https://aclanthology.org/2021.naacl-main.89
|
https://aclanthology.org/2021.naacl-main.89.pdf
|
NAACL 2021
|
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Macro-Average: Rare Types Are Important Too
|
Thamme Gowda, Weiqiu You, Constantine Lignos, Jonathan May
|
While traditional corpus-level evaluation metrics for machine translation (MT) correlate well with fluency, they struggle to reflect adequacy. Model-based MT metrics trained on segment-level human judgments have emerged as an attractive replacement due to strong correlation results. These models, however, require potentially expensive re-training for new domains and languages. Furthermore, their decisions are inherently non-transparent and appear to reflect unwelcome biases. We explore the simple type-based classifier metric, MacroF1, and study its applicability to MT evaluation. We find that MacroF1 is competitive on direct assessment, and outperforms others in indicating downstream cross-lingual information retrieval task performance. Further, we show that MacroF1 can be used to effectively compare supervised and unsupervised neural machine translation, and reveal significant qualitative differences in the methods’ outputs.
|
https://aclanthology.org/2021.naacl-main.90
|
https://aclanthology.org/2021.naacl-main.90.pdf
|
NAACL 2021
|
|||
Assessing Reference-Free Peer Evaluation for Machine Translation
|
Sweta Agrawal, George Foster, Markus Freitag, Colin Cherry
|
Reference-free evaluation has the potential to make machine translation evaluation substantially more scalable, allowing us to pivot easily to new languages or domains. It has been recently shown that the probabilities given by a large, multilingual model can achieve state of the art results when used as a reference-free metric. We experiment with various modifications to this model, and demonstrate that by scaling it up we can match the performance of BLEU. We analyze various potential weaknesses of the approach, and find that it is surprisingly robust and likely to offer reasonable performance across a broad spectrum of domains and different system qualities.
|
https://aclanthology.org/2021.naacl-main.91
|
https://aclanthology.org/2021.naacl-main.91.pdf
|
NAACL 2021
|
|||
The Curious Case of Hallucinations in Neural Machine Translation
|
Vikas Raunak, Arul Menezes, Marcin Junczys-Dowmunt
|
In this work, we study hallucinations in Neural Machine Translation (NMT), which lie at an extreme end on the spectrum of NMT pathologies. Firstly, we connect the phenomenon of hallucinations under source perturbation to the Long-Tail theory of Feldman, and present an empirically validated hypothesis that explains hallucinations under source perturbation. Secondly, we consider hallucinations under corpus-level noise (without any source perturbation) and demonstrate that two prominent types of natural hallucinations (detached and oscillatory outputs) could be generated and explained through specific corpus-level noise patterns. Finally, we elucidate the phenomenon of hallucination amplification in popular data-generation processes such as Backtranslation and sequence-level Knowledge Distillation. We have released the datasets and code to replicate our results.
|
https://aclanthology.org/2021.naacl-main.92
|
https://aclanthology.org/2021.naacl-main.92.pdf
|
NAACL 2021
|
|||
Towards Continual Learning for Multilingual Machine Translation via Vocabulary Substitution
|
Xavier Garcia, Noah Constant, Ankur Parikh, Orhan Firat
|
We propose a straightforward vocabulary adaptation scheme to extend the language capacity of multilingual machine translation models, paving the way towards efficient continual learning for multilingual machine translation. Our approach is suitable for large-scale datasets, applies to distant languages with unseen scripts, incurs only minor degradation on the translation performance for the original language pairs and provides competitive performance even in the case where we only possess monolingual data for the new languages.
|
https://aclanthology.org/2021.naacl-main.93
|
https://aclanthology.org/2021.naacl-main.93.pdf
|
NAACL 2021
|
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Towards Modeling the Style of Translators in Neural Machine Translation
|
Yue Wang, Cuong Hoang, Marcello Federico
|
One key ingredient of neural machine translation is the use of large datasets from different domains and resources (e.g. Europarl, TED talks). These datasets contain documents translated by professional translators using different but consistent translation styles. Despite that, the model is usually trained in a way that neither explicitly captures the variety of translation styles present in the data nor translates new data in different and controllable styles. In this work, we investigate methods to augment the state of the art Transformer model with translator information that is available in part of the training data. We show that our style-augmented translation models are able to capture the style variations of translators and to generate translations with different styles on new data. Indeed, the generated variations differ significantly, up to +4.5 BLEU score difference. Despite that, human evaluation confirms that the translations are of the same quality.
|
https://aclanthology.org/2021.naacl-main.94
|
https://aclanthology.org/2021.naacl-main.94.pdf
|
NAACL 2021
|
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Self-Supervised Test-Time Learning for Reading Comprehension
|
Pratyay Banerjee, Tejas Gokhale, Chitta Baral
|
{'i': 'context-question-answer', '#text': 'Recent work on unsupervised question answering has shown that models can be trained with procedurally generated question-answer pairs and can achieve performance competitive with supervised methods. In this work, we consider the task of unsupervised reading comprehension and present a method that performs “test-time learning” (TTL) on a given context (text passage), without requiring training on large-scale human-authored datasets containing triplets. This method operates directly on a single test context, uses self-supervision to train models on synthetically generated question-answer pairs, and then infers answers to unseen human-authored questions for this context. Our method achieves accuracies competitive with fully supervised methods and significantly outperforms current unsupervised methods. TTL methods with a smaller model are also competitive with the current state-of-the-art in unsupervised reading comprehension.'}
|
https://aclanthology.org/2021.naacl-main.95
|
https://aclanthology.org/2021.naacl-main.95.pdf
|
NAACL 2021
|
|||
Capturing Row and Column Semantics in Transformer Based Question Answering over Tables
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Michael Glass, Mustafa Canim, Alfio Gliozzo, Saneem Chemmengath, Vishwajeet Kumar, Rishav Chakravarti, Avi Sil, Feifei Pan, Samarth Bharadwaj, Nicolas Rodolfo Fauceglia
|
Transformer based architectures are recently used for the task of answering questions over tables. In order to improve the accuracy on this task, specialized pre-training techniques have been developed and applied on millions of open-domain web tables. In this paper, we propose two novel approaches demonstrating that one can achieve superior performance on table QA task without even using any of these specialized pre-training techniques. The first model, called RCI interaction, leverages a transformer based architecture that independently classifies rows and columns to identify relevant cells. While this model yields extremely high accuracy at finding cell values on recent benchmarks, a second model we propose, called RCI representation, provides a significant efficiency advantage for online QA systems over tables by materializing embeddings for existing tables. Experiments on recent benchmarks prove that the proposed methods can effectively locate cell values on tables (up to ~98% Hit@1 accuracy on WikiSQL lookup questions). Also, the interaction model outperforms the state-of-the-art transformer based approaches, pre-trained on very large table corpora (TAPAS and TaBERT), achieving ~3.4% and ~18.86% additional precision improvement on the standard WikiSQL benchmark.
|
https://aclanthology.org/2021.naacl-main.96
|
https://aclanthology.org/2021.naacl-main.96.pdf
|
NAACL 2021
|
|||
Explainable Multi-hop Verbal Reasoning Through Internal Monologue
|
Zhengzhong Liang, Steven Bethard, Mihai Surdeanu
|
Many state-of-the-art (SOTA) language models have achieved high accuracy on several multi-hop reasoning problems. However, these approaches tend to not be interpretable because they do not make the intermediate reasoning steps explicit. Moreover, models trained on simpler tasks tend to fail when directly tested on more complex problems. We propose the Explainable multi-hop Verbal Reasoner (EVR) to solve these limitations by (a) decomposing multi-hop reasoning problems into several simple ones, and (b) using natural language to guide the intermediate reasoning hops. We implement EVR by extending the classic reasoning paradigm General Problem Solver (GPS) with a SOTA generative language model to generate subgoals and perform inference in natural language at each reasoning step. Evaluation of EVR on the RuleTaker synthetic question answering (QA) dataset shows that EVR achieves SOTA performance while being able to generate all reasoning steps in natural language. Furthermore, EVR generalizes better than other strong methods when trained on simpler tasks or less training data (up to 35.7% and 7.7% absolute improvement respectively).
|
https://aclanthology.org/2021.naacl-main.97
|
https://aclanthology.org/2021.naacl-main.97.pdf
|
NAACL 2021
|
|||
Robust Question Answering Through Sub-part Alignment
|
Jifan Chen, Greg Durrett
|
Current textual question answering (QA) models achieve strong performance on in-domain test sets, but often do so by fitting surface-level patterns, so they fail to generalize to out-of-distribution settings. To make a more robust and understandable QA system, we model question answering as an alignment problem. We decompose both the question and context into smaller units based on off-the-shelf semantic representations (here, semantic roles), and align the question to a subgraph of the context in order to find the answer. We formulate our model as a structured SVM, with alignment scores computed via BERT, and we can train end-to-end despite using beam search for approximate inference. Our use of explicit alignments allows us to explore a set of constraints with which we can prohibit certain types of bad model behavior arising in cross-domain settings. Furthermore, by investigating differences in scores across different potential answers, we can seek to understand what particular aspects of the input lead the model to choose the answer without relying on post-hoc explanation techniques. We train our model on SQuAD v1.1 and test it on several adversarial and out-of-domain datasets. The results show that our model is more robust than the standard BERT QA model, and constraints derived from alignment scores allow us to effectively trade off coverage and accuracy.
|
https://aclanthology.org/2021.naacl-main.98
|
https://aclanthology.org/2021.naacl-main.98.pdf
|
NAACL 2021
|
|||
Text Modular Networks: Learning to Decompose Tasks in the Language of Existing Models
|
Tushar Khot, Daniel Khashabi, Kyle Richardson, Peter Clark, Ashish Sabharwal
|
{'i': 'language', '#text': 'We propose a general framework called Text Modular Networks(TMNs) for building interpretable systems that learn to solve complex tasks by decomposing them into simpler ones solvable by existing models. To ensure solvability of simpler tasks, TMNs learn the textual input-output behavior (i.e., ) of existing models through their datasets. This differs from prior decomposition-based approaches which, besides being designed specifically for each complex task, produce decompositions independent of existing sub-models. Specifically, we focus on Question Answering (QA) and show how to train a next-question generator to sequentially produce sub-questions targeting appropriate sub-models, without additional human annotation. These sub-questions and answers provide a faithful natural language explanation of the model’s reasoning. We use this framework to build ModularQA, a system that can answer multi-hop reasoning questions by decomposing them into sub-questions answerable by a neural factoid single-span QA model and a symbolic calculator. Our experiments show that ModularQA is more versatile than existing explainable systems for DROP and HotpotQA datasets, is more robust than state-of-the-art blackbox (uninterpretable) systems, and generates more understandable and trustworthy explanations compared to prior work.'}
|
https://aclanthology.org/2021.naacl-main.99
|
https://aclanthology.org/2021.naacl-main.99.pdf
|
NAACL 2021
|
|||
RECONSIDER: Improved Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering
|
Srinivasan Iyer, Sewon Min, Yashar Mehdad, Wen-tau Yih
|
State-of-the-art Machine Reading Comprehension (MRC) models for Open-domain Question Answering (QA) are typically trained for span selection using distantly supervised positive examples and heuristically retrieved negative examples. This training scheme possibly explains empirical observations that these models achieve a high recall amongst their top few predictions, but a low overall accuracy, motivating the need for answer re-ranking. We develop a successful re-ranking approach (RECONSIDER) for span-extraction tasks that improves upon the performance of MRC models, even beyond large-scale pre-training. RECONSIDER is trained on positive and negative examples extracted from high confidence MRC model predictions, and uses in-passage span annotations to perform span-focused re-ranking over a smaller candidate set. As a result, RECONSIDER learns to eliminate close false positives, achieving a new extractive state of the art on four QA tasks, with 45.5% Exact Match accuracy on Natural Questions with real user questions, and 61.7% on TriviaQA. We will release all related data, models, and code.
|
https://aclanthology.org/2021.naacl-main.100
|
https://aclanthology.org/2021.naacl-main.100.pdf
|
NAACL 2021
|
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