---
title: "CrossWOZ — paper by Zhu et al. (2020) — summary and ideas"
description: "The authors also release a rule-based user simulator and baselines for each pipeline module. 22 references, cited by 54 papers here."
authors: ["Qi Zhu", "Kaili Huang", "Zheng Zhang", "Xiaoyan Zhu", "Minlie Huang"]
year: 2020
venue: "Transactions of the Association for Computational Linguistics"
source: "https://aclanthology.org/2020.tacl-1.19/"
doi: "10.1162/tacl_a_00314"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhu-2020-crosswoz-large-scale-chinese-cross/"
---
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# CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset

**Authors:** Qi Zhu, Kaili Huang, [Zheng Zhang](https://md.research.elamur.ai/free/en/authors/zheng-zhang/), [Xiaoyan Zhu](https://md.research.elamur.ai/free/en/authors/xiaoyan-zhu/) and [Minlie Huang](https://md.research.elamur.ai/free/en/authors/minlie-huang/) · **Year:** 2020 · **Venue:** [Transactions of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/tacl/) ([Transactions of the Association for Computational Linguistics (TACL) 2020](https://md.research.elamur.ai/free/en/venues/tacl/2020/)) · **Publisher:** [The MIT Press](https://md.research.elamur.ai/free/en/publishers/the-mit-press/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2020.tacl-1.19/) · [PDF](https://aclanthology.org/2020.tacl-1.19.pdf) · [DOI 10.1162/tacl\_a\_00314](https://doi.org/10.1162/tacl_a_00314) · [OpenAlex](https://openalex.org/W3006892896)

**Citations:** 92 citations (OpenAlex, 2026-10-14) · [54](https://md.research.elamur.ai/free/en/papers/zhu-2020-crosswoz-large-scale-chinese-cross/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Task-oriented dialogue research has relied mainly on English data. Even multi-domain corpora such as MultiWOZ link domains only through fixed constraints set in advance, and they lack user-side annotation. CrossWOZ addresses both gaps. It is the first large-scale Chinese cross-domain dialogue corpus, collected in a Wizard-of-Oz, human-to-human setup that simulates tourists planning trips in Beijing.

A goal generator built on a crawled entity database creates linked sub-goals. For example, a user might need a hotel close to an attraction picked earlier, then a taxi between the two. Choices made in one domain therefore shape later domains as the conversation unfolds. Crowdworkers played both roles and recorded user and system states. Dialogue acts were then derived by rules, and an expert check showed the annotations to be of high quality.

The authors also release a rule-based user simulator and baselines for each pipeline module. Their experiments show that cross-domain goals are much harder to complete. The resource provides a Chinese testbed for state tracking, policy learning and user simulation.

## Key points
- Size and scope: about 6K dialogues and 102K utterances across 5 domains: hotel, restaurant, attraction, metro and taxi. Roughly 60% of the dialogues involve cross-domain goals.
- Complexity versus MultiWOZ: the training split has 5,012 dialogues, averaging 3.24 domains and 16.9 turns per dialogue. MultiWOZ averages 1.80 domains.
- Database: 465 attractions, 951 restaurants and 1,133 hotels. Annotation includes dialogue states and dialogue acts for both user and system.
- Baselines: BERTNLU, RuleDST, SL policy, TemplateNLG and SC-LSTM, plus a rule-based simulator. System-level results are limited mainly by RuleDST and SL policy. TemplateNLG has a lower BLEU score but outperforms SC-LSTM in simulation.
- Availability: data and code are at github.com/thu-coai/CrossWOZ, and the paper is licensed CC-BY 4.0.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Tsinghua University](https://md.research.elamur.ai/free/en/universities/tsinghua-university/) — University, Beijing, China · 137 works · 83,262 citations
- [Beijing National Research Center for Information Science and Technology](https://md.research.elamur.ai/free/en/labs/beijing-national-research-center-for-information-science-and-technology/) — Research institute, Beijing, China · 21 works · 5,843 citations

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## Cite

Cite as: Qi Zhu, Kaili Huang, Zheng Zhang, Xiaoyan Zhu and Minlie Huang. 2020. CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset. Transactions of the Association for Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@article{zhu2020crosswoz,
  title = {{CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset}},
  author = {Qi Zhu, Kaili Huang, Zheng Zhang, Xiaoyan Zhu and Minlie Huang},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2020},
  doi = {10.1162/tacl\_a\_00314},
  url = {https://doi.org/10.1162/tacl_a_00314}
}
```

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*Updated 24 October 2026*