| # Dataset Card for Taskmaster-1 | |
| - **Repository:** https://github.com/google-research-datasets/Taskmaster/tree/master/TM-1-2019 | |
| - **Paper:** https://arxiv.org/pdf/1909.05358.pdf | |
| - **Leaderboard:** None | |
| - **Who transforms the dataset:** Qi Zhu(zhuq96 at gmail dot com) | |
| ### Dataset Summary | |
| The original dataset consists of 13,215 task-based dialogs, including 5,507 spoken and 7,708 written dialogs created with two distinct procedures. Each conversation falls into one of six domains: ordering pizza, creating auto repair appointments, setting up ride service, ordering movie tickets, ordering coffee drinks and making restaurant reservations. | |
| - **How to get the transformed data from original data:** | |
| - Download [master.zip](https://github.com/google-research-datasets/Taskmaster/archive/refs/heads/master.zip). | |
| - Run `python preprocess.py` in the current directory. | |
| - **Main changes of the transformation:** | |
| - Remove dialogs that are empty or only contain one speaker. | |
| - Split woz-dialogs into train/validation/test randomly (8:1:1). The split of self-dialogs is followed the original dataset. | |
| - Merge continuous turns by the same speaker (ignore repeated turns). | |
| - Annotate `dialogue acts` according to the original segment annotations. Add `intent` annotation (inform/accept/reject). The type of `dialogue act` is set to `non-categorical` if the original segment annotation includes a specified `slot`. Otherwise, the type is set to `binary` (and the `slot` and `value` are empty) since it means general reference to a transaction, e.g. "OK your pizza has been ordered". If there are multiple spans overlapping, we only keep the shortest one, since we found that this simple strategy can reduce the noise in annotation. | |
| - Add `domain`, `intent`, and `slot` descriptions. | |
| - Add `state` by accumulate `non-categorical dialogue acts` in the order that they appear, except those whose intents are **reject**. | |
| - Keep the first annotation since each conversation was annotated by two workers. | |
| - **Annotations:** | |
| - dialogue acts, state. | |
| ### Supported Tasks and Leaderboards | |
| NLU, DST, Policy, NLG | |
| ### Languages | |
| English | |
| ### Data Splits | |
| | split | dialogues | utterances | avg_utt | avg_tokens | avg_domains | cat slot match(state) | cat slot match(goal) | cat slot match(dialogue act) | non-cat slot span(dialogue act) | | |
| |------------|-------------|--------------|-----------|--------------|---------------|-------------------------|------------------------|--------------------------------|-----------------------------------| | |
| | train | 10535 | 223322 | 21.2 | 8.75 | 1 | - | - | - | 100 | | |
| | validation | 1318 | 27903 | 21.17 | 8.75 | 1 | - | - | - | 100 | | |
| | test | 1322 | 27660 | 20.92 | 8.87 | 1 | - | - | - | 100 | | |
| | all | 13175 | 278885 | 21.17 | 8.76 | 1 | - | - | - | 100 | | |
| 6 domains: ['uber_lyft', 'movie_ticket', 'restaurant_reservation', 'coffee_ordering', 'pizza_ordering', 'auto_repair'] | |
| - **cat slot match**: how many values of categorical slots are in the possible values of ontology in percentage. | |
| - **non-cat slot span**: how many values of non-categorical slots have span annotation in percentage. | |
| ### Citation | |
| ``` | |
| @inproceedings{byrne-etal-2019-taskmaster, | |
| title = {Taskmaster-1:Toward a Realistic and Diverse Dialog Dataset}, | |
| author = {Bill Byrne and Karthik Krishnamoorthi and Chinnadhurai Sankar and Arvind Neelakantan and Daniel Duckworth and Semih Yavuz and Ben Goodrich and Amit Dubey and Kyu-Young Kim and Andy Cedilnik}, | |
| booktitle = {2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing}, | |
| address = {Hong Kong}, | |
| year = {2019} | |
| } | |
| ``` | |
| ### Licensing Information | |
| [**CC BY 4.0**](https://creativecommons.org/licenses/by/4.0/) |