CDConv: A Benchmark for Contradiction Detection in Chinese Conversations
Chujie Zheng | Jinfeng Zhou | Yinhe Zheng | Libiao Peng | Zhen Guo | Wenquan Wu | Zheng-Yu Niu | Hua Wu | Minlie Huang
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Dialogue contradiction is a critical issue in open-domain dialogue systems. The contextualization nature of conversations makes dialogue contradiction detection rather challenging. In this work, we propose a benchmark for Contradiction Detection in Chinese Conversations, namely CDConv. It contains 12K multi-turn conversations annotated with three typical contradiction categories: Intra-sentence Contradiction, Role Confusion, and History Contradiction. To efficiently construct the CDConv conversations, we devise a series of methods for automatic conversation generation, which simulate common user behaviors that trigger chatbots to make contradictions. We conduct careful manual quality screening of the constructed conversations and show that state-of-the-art Chinese chatbots can be easily goaded into making contradictions. Experiments on CDConv show that properly modeling contextual information is critical for dialogue contradiction detection, but there are still unresolved challenges that require future research.
- Chujie Zheng 1
- Jinfeng Zhou 1
- Yinhe Zheng 1
- Zhen Guo 1
- Wenquan Wu 1
- show all...