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---
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 |