@inproceedings{wu-etal-2022-disentangling,
title = "Disentangling Confidence Score Distribution for Out-of-Domain Intent Detection with Energy-Based Learning",
author = "Wu, Yanan and
Zeng, Zhiyuan and
He, Keqing and
Mou, Yutao and
Wang, Pei and
Yan, Yuanmeng and
Xu, Weiran",
editor = "Ou, Zhijian and
Feng, Junlan and
Li, Juanzi",
booktitle = "Proceedings of the Towards Semi-Supervised and Reinforced Task-Oriented Dialog Systems (SereTOD)",
month = dec,
year = "2022",
address = "Abu Dhabi, Beijing (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.seretod-1.5",
doi = "10.18653/v1/2022.seretod-1.5",
pages = "31--38",
abstract = "Detecting Out-of-Domain (OOD) or unknown intents from user queries is essential in a taskoriented dialog system. Traditional softmaxbased confidence scores are susceptible to the overconfidence issue. In this paper, we propose a simple but strong energy-based score function to detect OOD where the energy scores of OOD samples are higher than IND samples. Further, given a small set of labeled OOD samples, we introduce an energy-based margin objective for supervised OOD detection to explicitly distinguish OOD samples from INDs. Comprehensive experiments and analysis prove our method helps disentangle confidence score distributions of IND and OOD data.",
}
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<abstract>Detecting Out-of-Domain (OOD) or unknown intents from user queries is essential in a taskoriented dialog system. Traditional softmaxbased confidence scores are susceptible to the overconfidence issue. In this paper, we propose a simple but strong energy-based score function to detect OOD where the energy scores of OOD samples are higher than IND samples. Further, given a small set of labeled OOD samples, we introduce an energy-based margin objective for supervised OOD detection to explicitly distinguish OOD samples from INDs. Comprehensive experiments and analysis prove our method helps disentangle confidence score distributions of IND and OOD data.</abstract>
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%0 Conference Proceedings
%T Disentangling Confidence Score Distribution for Out-of-Domain Intent Detection with Energy-Based Learning
%A Wu, Yanan
%A Zeng, Zhiyuan
%A He, Keqing
%A Mou, Yutao
%A Wang, Pei
%A Yan, Yuanmeng
%A Xu, Weiran
%Y Ou, Zhijian
%Y Feng, Junlan
%Y Li, Juanzi
%S Proceedings of the Towards Semi-Supervised and Reinforced Task-Oriented Dialog Systems (SereTOD)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, Beijing (Hybrid)
%F wu-etal-2022-disentangling
%X Detecting Out-of-Domain (OOD) or unknown intents from user queries is essential in a taskoriented dialog system. Traditional softmaxbased confidence scores are susceptible to the overconfidence issue. In this paper, we propose a simple but strong energy-based score function to detect OOD where the energy scores of OOD samples are higher than IND samples. Further, given a small set of labeled OOD samples, we introduce an energy-based margin objective for supervised OOD detection to explicitly distinguish OOD samples from INDs. Comprehensive experiments and analysis prove our method helps disentangle confidence score distributions of IND and OOD data.
%R 10.18653/v1/2022.seretod-1.5
%U https://aclanthology.org/2022.seretod-1.5
%U https://doi.org/10.18653/v1/2022.seretod-1.5
%P 31-38
Markdown (Informal)
[Disentangling Confidence Score Distribution for Out-of-Domain Intent Detection with Energy-Based Learning](https://aclanthology.org/2022.seretod-1.5) (Wu et al., SereTOD 2022)
ACL