CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset

CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset Qi Zhu1, Kaili Huang2, Zheng Zhang1, Xiaoyan Zhu1, Minlie Huang1∗ 1Dept. of Computer Science and Technology, 1Institute for Artificial Intelligence, 1Beijing National Research Center for Information Science and Technology, 2Dept. of Industrial Engineering, Tsinghua University, Beijing, China {zhu-q18,hkl16,z-zhang15}@mails.tsinghua.edu.cn {zxy-dcs,aihuang}@tsinghua.edu.cn Abstract To advance multi-domain (cross-domain) dia- logue modeling as well as alleviate the short- age of Chinese task-oriented datasets, we proposeCrossWOZ,thefirst large-scale Chinese Cross-DomainWizard-of-Oz task-oriented data- set. 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 conversa- tion. 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. 1 Introduction Recently, there have been a variety of task-oriented dialogue models thanks to the prosperity of neural architectures (Yao et al., 2013; Wen et al., 2015; Mrkˇsi´c et al., 2017; Peng et al., 2017; Lei et al., 2018; G¨ur et al., 2018). However, research is still largely limited by the lack of large-scale high- quality dialogue data. Many corpora have advanced the research of task-oriented dialogue systems, most of which are single domain conversations, including ATIS (Hemphill et al., 1990), DSTC 2 (Henderson et al., 2014), Frames (El Asri et al., 2017), KVRET (Eric et al., 2017), WOZ 2.0 (Wen et al., 2017), and M2M (Shah et al., 2018). ∗Corresponding author. Despite the significant contributions to the community, these datasets are still limited in size, language variation, or task complexity. Further- more, there is a gap between existing dialogue corpora and real-life human dialogue data. In real-life conversations, it is natural for humans to transition between different domains or scenarios while still maintaining coherent contexts. Thus, real-life dialogues are much more complicated than those dialogues that are only simulated within a single domain. To address this issue, some multi-domain corpora have been proposed (Budzianowski et al., 2018b; Rastogi et al., 2019). The most notable corpus is MultiWOZ (Budzianowski et al., 2018b), a large-scale multi- domain dataset that consists of crowdsourced human-to-human dialogues. It contains 10K dialogue sessions and 143K utterances for 7 domains, with annotation of system-side dialogue states and dialogue acts. However, the state annotations are noisy (Eric et al., 2019), and user- side dialogue acts are missing. The dependency across domains is simply embodied in imposing the same pre-specified constraints on different domains, such as requiring both a hotel and an attraction to locate in the center of the town. In comparison to the abundance of English dialogue data, surprisingly, there is still no widely …

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