CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset
This dataset addresses a gap for researchers in multi-domain dialogue modeling, particularly in Chinese, though it is incremental as it builds on existing Wizard-of-Oz methods.
The authors tackled the shortage of Chinese task-oriented dialogue datasets by introducing CrossWOZ, a large-scale dataset with 6K dialogue sessions and 102K utterances across 5 domains, featuring rich annotations and cross-domain goals.
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 at 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.