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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-2 |
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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-2 |
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- **Repository:** https://github.com/google-research-datasets/Taskmaster/tree/master/TM-2-2020 |
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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('tm2') |
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ontology = load_ontology('tm2') |
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database = load_database('tm2') |
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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 Taskmaster-2 dataset consists of 17,289 dialogs in the seven domains. Unlike Taskmaster-1, which includes both written "self-dialogs" and spoken two-person dialogs, Taskmaster-2 consists entirely of spoken two-person dialogs. In addition, while Taskmaster-1 is almost exclusively task-based, Taskmaster-2 contains a good number of search- and recommendation-oriented dialogs, as seen for example in the restaurants, flights, hotels, and movies verticals. The music browsing and sports conversations are almost exclusively search- and recommendation-based. All dialogs in this release were created using a Wizard of Oz (WOz) methodology in which crowdsourced workers played the role of a 'user' and trained call center operators played the role of the 'assistant'. In this way, users were led to believe they were interacting with an automated system that “spoke” using text-to-speech (TTS) even though it was in fact a human behind the scenes. As a result, users could express themselves however they chose in the context of an automated interface. |
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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 each domain dialogs into train/validation/test randomly (8:1:1). |
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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`). The type of `dialogue act` is set to `non-categorical` if the `slot` is not in `anno2slot` in `preprocess.py`). Otherwise, the type is set to `binary` (and the `value` is empty). 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. |
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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 | 13838 | 234321 | 16.93 | 9.1 | 1 | - | - | - | 100 | |
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| validation | 1731 | 29349 | 16.95 | 9.15 | 1 | - | - | - | 100 | |
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| test | 1734 | 29447 | 16.98 | 9.07 | 1 | - | - | - | 100 | |
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| all | 17303 | 293117 | 16.94 | 9.1 | 1 | - | - | - | 100 | |
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7 domains: ['flights', 'food-ordering', 'hotels', 'movies', 'music', 'restaurant-search', 'sports'] |
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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/) |
