@inproceedings{zheng-etal-2022-cdconv,
    title = "{CDC}onv: A Benchmark for Contradiction Detection in {C}hinese Conversations",
    author = "Zheng, Chujie  and
      Zhou, Jinfeng  and
      Zheng, Yinhe  and
      Peng, Libiao  and
      Guo, Zhen  and
      Wu, Wenquan  and
      Niu, Zheng-Yu  and
      Wu, Hua  and
      Huang, Minlie",
    editor = "Goldberg, Yoav  and
      Kozareva, Zornitsa  and
      Zhang, Yue",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.emnlp-main.2",
    doi = "10.18653/v1/2022.emnlp-main.2",
    pages = "18--29",
    abstract = "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.",
}