@inproceedings{wu-etal-2022-revisit,
title = "Revisit Overconfidence for {OOD} Detection: Reassigned Contrastive Learning with Adaptive Class-dependent Threshold",
author = "Wu, Yanan and
He, Keqing and
Yan, Yuanmeng and
Gao, QiXiang and
Zeng, Zhiyuan and
Zheng, Fujia and
Zhao, Lulu and
Jiang, Huixing and
Wu, Wei and
Xu, Weiran",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.307",
doi = "10.18653/v1/2022.naacl-main.307",
pages = "4165--4179",
abstract = "Detecting Out-of-Domain (OOD) or unknown intents from user queries is essential in a task-oriented dialog system. A key challenge of OOD detection is the overconfidence of neural models. In this paper, we comprehensively analyze overconfidence and classify it into two perspectives: over-confident OOD and in-domain (IND). Then according to intrinsic reasons, we respectively propose a novel reassigned contrastive learning (RCL) to discriminate IND intents for over-confident OOD and an adaptive class-dependent local threshold mechanism to separate similar IND and OOD intents for over-confident IND. Experiments and analyses show the effectiveness of our proposed method for both aspects of overconfidence issues.",
}
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<abstract>Detecting Out-of-Domain (OOD) or unknown intents from user queries is essential in a task-oriented dialog system. A key challenge of OOD detection is the overconfidence of neural models. In this paper, we comprehensively analyze overconfidence and classify it into two perspectives: over-confident OOD and in-domain (IND). Then according to intrinsic reasons, we respectively propose a novel reassigned contrastive learning (RCL) to discriminate IND intents for over-confident OOD and an adaptive class-dependent local threshold mechanism to separate similar IND and OOD intents for over-confident IND. Experiments and analyses show the effectiveness of our proposed method for both aspects of overconfidence issues.</abstract>
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%0 Conference Proceedings
%T Revisit Overconfidence for OOD Detection: Reassigned Contrastive Learning with Adaptive Class-dependent Threshold
%A Wu, Yanan
%A He, Keqing
%A Yan, Yuanmeng
%A Gao, QiXiang
%A Zeng, Zhiyuan
%A Zheng, Fujia
%A Zhao, Lulu
%A Jiang, Huixing
%A Wu, Wei
%A Xu, Weiran
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F wu-etal-2022-revisit
%X Detecting Out-of-Domain (OOD) or unknown intents from user queries is essential in a task-oriented dialog system. A key challenge of OOD detection is the overconfidence of neural models. In this paper, we comprehensively analyze overconfidence and classify it into two perspectives: over-confident OOD and in-domain (IND). Then according to intrinsic reasons, we respectively propose a novel reassigned contrastive learning (RCL) to discriminate IND intents for over-confident OOD and an adaptive class-dependent local threshold mechanism to separate similar IND and OOD intents for over-confident IND. Experiments and analyses show the effectiveness of our proposed method for both aspects of overconfidence issues.
%R 10.18653/v1/2022.naacl-main.307
%U https://aclanthology.org/2022.naacl-main.307
%U https://doi.org/10.18653/v1/2022.naacl-main.307
%P 4165-4179
Markdown (Informal)
[Revisit Overconfidence for OOD Detection: Reassigned Contrastive Learning with Adaptive Class-dependent Threshold](https://aclanthology.org/2022.naacl-main.307) (Wu et al., NAACL 2022)
ACL
- Yanan Wu, Keqing He, Yuanmeng Yan, QiXiang Gao, Zhiyuan Zeng, Fujia Zheng, Lulu Zhao, Huixing Jiang, Wei Wu, and Weiran Xu. 2022. Revisit Overconfidence for OOD Detection: Reassigned Contrastive Learning with Adaptive Class-dependent Threshold. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4165–4179, Seattle, United States. Association for Computational Linguistics.