@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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T CDConv: A Benchmark for Contradiction Detection in Chinese Conversations
%A Zheng, Chujie
%A Zhou, Jinfeng
%A Zheng, Yinhe
%A Peng, Libiao
%A Guo, Zhen
%A Wu, Wenquan
%A Niu, Zheng-Yu
%A Wu, Hua
%A Huang, Minlie
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F zheng-etal-2022-cdconv
%X 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.
%R 10.18653/v1/2022.emnlp-main.2
%U https://aclanthology.org/2022.emnlp-main.2
%U https://doi.org/10.18653/v1/2022.emnlp-main.2
%P 18-29
Markdown (Informal)
[CDConv: A Benchmark for Contradiction Detection in Chinese Conversations](https://aclanthology.org/2022.emnlp-main.2) (Zheng et al., EMNLP 2022)
ACL
- Chujie Zheng, Jinfeng Zhou, Yinhe Zheng, Libiao Peng, Zhen Guo, Wenquan Wu, Zheng-Yu Niu, Hua Wu, and Minlie Huang. 2022. CDConv: A Benchmark for Contradiction Detection in Chinese Conversations. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 18–29, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.