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        <title>CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset</title>
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    <name type="personal">
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    <abstract>To advance multi-domain (cross-domain) dialogue modeling as well as alleviate the shortage of Chinese task-oriented datasets, we propose CrossWOZ, 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 on both user and system sides. About 60% of the dialogues have cross-domain user goals that favor inter-domain dependency and encourage natural transition across domains in conversation. We also provide a user simulator and several benchmark models for pipelined task-oriented dialogue systems, which will facilitate researchers to compare and evaluate their models on this corpus. The large size and rich annotation of CrossWOZ make it suitable to investigate a variety of tasks in cross-domain dialogue modeling, such as dialogue state tracking, policy learning, user simulation, etc.</abstract>
    <identifier type="citekey">zhu-etal-2020-crosswoz</identifier>
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%0 Journal Article
%T CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset
%A Zhu, Qi
%A Huang, Kaili
%A Zhang, Zheng
%A Zhu, Xiaoyan
%A Huang, Minlie
%J Transactions of the Association for Computational Linguistics
%D 2020
%V 8
%I MIT Press
%C Cambridge, MA
%F zhu-etal-2020-crosswoz
%X To advance multi-domain (cross-domain) dialogue modeling as well as alleviate the shortage of Chinese task-oriented datasets, we propose CrossWOZ, 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 on both user and system sides. About 60% of the dialogues have cross-domain user goals that favor inter-domain dependency and encourage natural transition across domains in conversation. We also provide a user simulator and several benchmark models for pipelined task-oriented dialogue systems, which will facilitate researchers to compare and evaluate their models on this corpus. The large size and rich annotation of CrossWOZ make it suitable to investigate a variety of tasks in cross-domain dialogue modeling, such as dialogue state tracking, policy learning, user simulation, etc.
%R 10.1162/tacl_a_00314
%U https://aclanthology.org/2020.tacl-1.19/
%U https://doi.org/10.1162/tacl_a_00314
%P 281-295

Markdown (Informal)

[CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset](https://aclanthology.org/2020.tacl-1.19/) (Zhu et al., TACL 2020)

• CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset (Zhu et al., TACL 2020)

ACL

• 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, 8:281–295.