| --- |
| language: |
| - zh |
| license: |
| - apache-2.0 |
| multilinguality: |
| - monolingual |
| pretty_name: CrossWOZ |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - conversational |
| --- |
| |
| # Dataset Card for CrossWOZ |
|
|
| - **Repository:** https://github.com/thu-coai/CrossWOZ |
| - **Paper:** https://aclanthology.org/2020.tacl-1.19/ |
| - **Leaderboard:** None |
| - **Who transforms the dataset:** Qi Zhu(zhuq96 at gmail dot com) |
|
|
| 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: |
| ``` |
| from convlab.util import load_dataset, load_ontology, load_database |
| |
| dataset = load_dataset('crosswoz') |
| ontology = load_ontology('crosswoz') |
| database = load_database('crosswoz') |
| ``` |
| For more usage please refer to [here](https://github.com/ConvLab/ConvLab-3/tree/master/data/unified_datasets). |
|
|
| ### Dataset Summary |
|
|
| 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. |
|
|
| - **How to get the transformed data from original data:** |
| - Run `python preprocess.py` in the current directory. Need `../../crosswoz/` as the original data. |
| - **Main changes of the transformation:** |
| - Add simple description for domains, slots, and intents. |
| - Switch intent&domain of `General` dialog acts => domain == 'General' and intent in ['thank','bye','greet','welcome'] |
| - Binary dialog acts include: 1) domain == 'General'; 2) intent in ['NoOffer', 'Request', 'Select']; 3) slot in ['酒店设施'] |
| - Categorical dialog acts include: slot in ['酒店类型', '车型', '车牌'] |
| - Non-categorical dialogue acts: others. assert intent in ['Inform', 'Recommend'] and slot != 'none' and value != 'none' |
| - 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. |
| - Transform `sys_state_init` (first API call of system turns) without `selectedResults` as belief state in user turns. |
| - 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. |
| - **Annotations:** |
| - user goal, user state, dialogue acts, state, db query, db results. |
| - Multiple values in state are separated by spaces, meaning all constraints should be satisfied. |
|
|
| ### Supported Tasks and Leaderboards |
|
|
| NLU, DST, Policy, NLG, E2E, User simulator |
|
|
| ### Languages |
|
|
| Chinese |
|
|
| ### 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 | 5012 | 84674 | 16.89 | 20.55 | 3.02 | 99.67 | - | 100 | 94.39 | |
| | validation | 500 | 8458 | 16.92 | 20.53 | 3.04 | 99.62 | - | 100 | 94.36 | |
| | test | 500 | 8476 | 16.95 | 20.51 | 3.08 | 99.61 | - | 100 | 94.85 | |
| | all | 6012 | 101608 | 16.9 | 20.54 | 3.03 | 99.66 | - | 100 | 94.43 | |
| |
| 6 domains: ['景点', '餐馆', '酒店', '地铁', '出租', 'General'] |
| - **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 |
| |
| ``` |
| @article{zhu2020crosswoz, |
| author = {Qi Zhu and Kaili Huang and Zheng Zhang and Xiaoyan Zhu and Minlie Huang}, |
| title = {Cross{WOZ}: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset}, |
| journal = {Transactions of the Association for Computational Linguistics}, |
| year = {2020} |
| } |
| ``` |
| |
| ### Licensing Information |
| |
| Apache License, Version 2.0 |
