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--- |
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language: |
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- zh |
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license: |
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- apache-2.0 |
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multilinguality: |
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- monolingual |
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pretty_name: CrossWOZ |
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size_categories: |
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- 1K<n<10K |
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task_categories: |
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- conversational |
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--- |
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# Dataset Card for CrossWOZ |
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- **Repository:** https://github.com/thu-coai/CrossWOZ |
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- **Paper:** https://aclanthology.org/2020.tacl-1.19/ |
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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('crosswoz') |
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ontology = load_ontology('crosswoz') |
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database = load_database('crosswoz') |
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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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CrossWOZ is the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restaurant, attraction, metro, and taxi. Moreover, the corpus contains rich annotation of dialogue states and dialogue acts at both user and system sides. We also provide a user simulator and several benchmark models for pipelined taskoriented dialogue systems, which will facilitate researchers to compare and evaluate their models on this corpus. |
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- **How to get the transformed data from original data:** |
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- Run `python preprocess.py` in the current directory. Need `../../crosswoz/` as the original data. |
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- **Main changes of the transformation:** |
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- Add simple description for domains, slots, and intents. |
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- Switch intent&domain of `General` dialog acts => domain == 'General' and intent in ['thank','bye','greet','welcome'] |
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- Binary dialog acts include: 1) domain == 'General'; 2) intent in ['NoOffer', 'Request', 'Select']; 3) slot in ['酒店设施'] |
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- Categorical dialog acts include: slot in ['酒店类型', '车型', '车牌'] |
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- Non-categorical dialogue acts: others. assert intent in ['Inform', 'Recommend'] and slot != 'none' and value != 'none' |
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- Transform original user goal to list of `{domain: {'inform': {slot: [value, mentioned/not mentioned]}, 'request': {slot: [value, mentioned/not mentioned]}}}`, stored as `user_state` of user turns. |
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- Transform `sys_state_init` (first API call of system turns) without `selectedResults` as belief state in user turns. |
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- Transform `sys_state` (last API call of system turns) to `db_query` with domain states that contain non-empty `selectedResults`. The `selectedResults` are saved as `db_results` (only contain entity name). Both stored in system turns. |
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- **Annotations:** |
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- user goal, user state, dialogue acts, state, db query, db results. |
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- Multiple values in state are separated by spaces, meaning all constraints should be satisfied. |
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### Supported Tasks and Leaderboards |
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NLU, DST, Policy, NLG, E2E, User simulator |
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### Languages |
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Chinese |
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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 | 5012 | 84674 | 16.89 | 20.55 | 3.02 | 99.67 | - | 100 | 94.39 | |
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| validation | 500 | 8458 | 16.92 | 20.53 | 3.04 | 99.62 | - | 100 | 94.36 | |
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| test | 500 | 8476 | 16.95 | 20.51 | 3.08 | 99.61 | - | 100 | 94.85 | |
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| all | 6012 | 101608 | 16.9 | 20.54 | 3.03 | 99.66 | - | 100 | 94.43 | |
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6 domains: ['景点', '餐馆', '酒店', '地铁', '出租', 'General'] |
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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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@article{zhu2020crosswoz, |
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author = {Qi Zhu and Kaili Huang and Zheng Zhang and Xiaoyan Zhu and Minlie Huang}, |
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title = {Cross{WOZ}: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset}, |
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journal = {Transactions of the Association for Computational Linguistics}, |
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year = {2020} |
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} |
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``` |
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### Licensing Information |
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Apache License, Version 2.0 |
