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--- |
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language: |
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- en |
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license: |
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- cc-by-4.0 |
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multilinguality: |
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- monolingual |
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pretty_name: Taskmaster-1 |
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size_categories: |
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- 10K<n<100K |
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task_categories: |
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- conversational |
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--- |
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# Dataset Card for Taskmaster-1 |
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- **Repository:** https://github.com/google-research-datasets/Taskmaster/tree/master/TM-1-2019 |
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- **Paper:** https://arxiv.org/pdf/1909.05358.pdf |
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- **Leaderboard:** None |
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- **Who transforms the dataset:** Qi Zhu(zhuq96 at gmail dot com) |
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To use this dataset, you need to install [ConvLab-3](https://github.com/ConvLab/ConvLab-3) platform first. Then you can load the dataset via: |
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``` |
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from convlab.util import load_dataset, load_ontology, load_database |
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dataset = load_dataset('tm1') |
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ontology = load_ontology('tm1') |
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database = load_database('tm1') |
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``` |
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For more usage please refer to [here](https://github.com/ConvLab/ConvLab-3/tree/master/data/unified_datasets). |
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### Dataset Summary |
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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. |
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- **How to get the transformed data from original data:** |
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- Download [master.zip](https://github.com/google-research-datasets/Taskmaster/archive/refs/heads/master.zip). |
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- Run `python preprocess.py` in the current directory. |
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- **Main changes of the transformation:** |
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- Remove dialogs that are empty or only contain one speaker. |
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- Split woz-dialogs into train/validation/test randomly (8:1:1). The split of self-dialogs is followed the original dataset. |
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- Merge continuous turns by the same speaker (ignore repeated turns). |
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- 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. |
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- Add `domain`, `intent`, and `slot` descriptions. |
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- Add `state` by accumulate `non-categorical dialogue acts` in the order that they appear, except those whose intents are **reject**. |
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- Keep the first annotation since each conversation was annotated by two workers. |
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- **Annotations:** |
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- dialogue acts, state. |
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### Supported Tasks and Leaderboards |
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NLU, DST, Policy, NLG |
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### Languages |
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English |
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### Data Splits |
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| 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) | |
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|------------|-------------|--------------|-----------|--------------|---------------|-------------------------|------------------------|--------------------------------|-----------------------------------| |
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| train | 10535 | 223322 | 21.2 | 8.75 | 1 | - | - | - | 100 | |
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| validation | 1318 | 27903 | 21.17 | 8.75 | 1 | - | - | - | 100 | |
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| test | 1322 | 27660 | 20.92 | 8.87 | 1 | - | - | - | 100 | |
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| all | 13175 | 278885 | 21.17 | 8.76 | 1 | - | - | - | 100 | |
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6 domains: ['uber_lyft', 'movie_ticket', 'restaurant_reservation', 'coffee_ordering', 'pizza_ordering', 'auto_repair'] |
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- **cat slot match**: how many values of categorical slots are in the possible values of ontology in percentage. |
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- **non-cat slot span**: how many values of non-categorical slots have span annotation in percentage. |
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### Citation |
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``` |
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@inproceedings{byrne-etal-2019-taskmaster, |
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title = {Taskmaster-1:Toward a Realistic and Diverse Dialog Dataset}, |
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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}, |
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booktitle = {2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing}, |
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address = {Hong Kong}, |
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year = {2019} |
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} |
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``` |
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### Licensing Information |
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[**CC BY 4.0**](https://creativecommons.org/licenses/by/4.0/) |
