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Dialogue contradiction is a critical issue in open-domain dialogue systems.The contextualization nature of conversations makes dialogue contradictiondetection rather challenging. In this work, we propose a benchmark forContradiction Detection in Chinese Conversations, namely CDConv. It contains12K multi-turn conversations annotated with three typical contradictioncategories: Intra-sentence Contradiction, Role Confusion, and HistoryContradiction. To efficiently construct the CDConv conversations, we devise aseries of methods for automatic conversation generation, which simulate commonuser behaviors that trigger chatbots to make contradictions. We conduct carefulmanual quality screening of the constructed conversations and show thatstate-of-the-art Chinese chatbots can be easily goaded into makingcontradictions. Experiments on CDConv show that properly modeling contextualinformation is critical for dialogue contradiction detection, but there arestill unresolved challenges that require future research.

CDConv: A Benchmark for Contradiction Detection in Chinese Conversations