Distribution Calibration for Out-of-Domain Detection with Bayesian Approximation
Yanan Wu, Zhiyuan Zeng, Keqing He, Yutao Mou, Pei Wang, Weiran Xu
Correct Metadata for
Abstract
Out-of-Domain (OOD) detection is a key component in a task-oriented dialog system, which aims to identify whether a query falls outside the predefined supported intent set. Previous softmax-based detection algorithms are proved to be overconfident for OOD samples. In this paper, we analyze overconfident OOD comes from distribution uncertainty due to the mismatch between the training and test distributions, which makes the model can’t confidently make predictions thus probably causes abnormal softmax scores. We propose a Bayesian OOD detection framework to calibrate distribution uncertainty using Monte-Carlo Dropout. Our method is flexible and easily pluggable to existing softmax-based baselines and gains 33.33% OOD F1 improvements with increasing only 0.41% inference time compared to MSP. Further analyses show the effectiveness of Bayesian learning for OOD detection.- Anthology ID:
- 2022.coling-1.50
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 608–615
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.50/
- DOI:
- Bibkey:
- Cite (ACL):
- Yanan Wu, Zhiyuan Zeng, Keqing He, Yutao Mou, Pei Wang, and Weiran Xu. 2022. Distribution Calibration for Out-of-Domain Detection with Bayesian Approximation. In Proceedings of the 29th International Conference on Computational Linguistics, pages 608–615, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Distribution Calibration for Out-of-Domain Detection with Bayesian Approximation (Wu et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.50.pdf
- Code
- pris-nlp/coling2022_bayesian-for-ood
Export citation
@inproceedings{wu-etal-2022-distribution,
title = "Distribution Calibration for Out-of-Domain Detection with {B}ayesian Approximation",
author = "Wu, Yanan and
Zeng, Zhiyuan and
He, Keqing and
Mou, Yutao and
Wang, Pei and
Xu, Weiran",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.50/",
pages = "608--615",
abstract = "Out-of-Domain (OOD) detection is a key component in a task-oriented dialog system, which aims to identify whether a query falls outside the predefined supported intent set. Previous softmax-based detection algorithms are proved to be overconfident for OOD samples. In this paper, we analyze overconfident OOD comes from distribution uncertainty due to the mismatch between the training and test distributions, which makes the model can{'}t confidently make predictions thus probably causes abnormal softmax scores. We propose a Bayesian OOD detection framework to calibrate distribution uncertainty using Monte-Carlo Dropout. Our method is flexible and easily pluggable to existing softmax-based baselines and gains 33.33{\%} OOD F1 improvements with increasing only 0.41{\%} inference time compared to MSP. Further analyses show the effectiveness of Bayesian learning for OOD detection."
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%0 Conference Proceedings %T Distribution Calibration for Out-of-Domain Detection with Bayesian Approximation %A Wu, Yanan %A Zeng, Zhiyuan %A He, Keqing %A Mou, Yutao %A Wang, Pei %A Xu, Weiran %Y Calzolari, Nicoletta %Y Huang, Chu-Ren %Y Kim, Hansaem %Y Pustejovsky, James %Y Wanner, Leo %Y Choi, Key-Sun %Y Ryu, Pum-Mo %Y Chen, Hsin-Hsi %Y Donatelli, Lucia %Y Ji, Heng %Y Kurohashi, Sadao %Y Paggio, Patrizia %Y Xue, Nianwen %Y Kim, Seokhwan %Y Hahm, Younggyun %Y He, Zhong %Y Lee, Tony Kyungil %Y Santus, Enrico %Y Bond, Francis %Y Na, Seung-Hoon %S Proceedings of the 29th International Conference on Computational Linguistics %D 2022 %8 October %I International Committee on Computational Linguistics %C Gyeongju, Republic of Korea %F wu-etal-2022-distribution %X Out-of-Domain (OOD) detection is a key component in a task-oriented dialog system, which aims to identify whether a query falls outside the predefined supported intent set. Previous softmax-based detection algorithms are proved to be overconfident for OOD samples. In this paper, we analyze overconfident OOD comes from distribution uncertainty due to the mismatch between the training and test distributions, which makes the model can’t confidently make predictions thus probably causes abnormal softmax scores. We propose a Bayesian OOD detection framework to calibrate distribution uncertainty using Monte-Carlo Dropout. Our method is flexible and easily pluggable to existing softmax-based baselines and gains 33.33% OOD F1 improvements with increasing only 0.41% inference time compared to MSP. Further analyses show the effectiveness of Bayesian learning for OOD detection. %U https://aclanthology.org/2022.coling-1.50/ %P 608-615
Markdown (Informal)
[Distribution Calibration for Out-of-Domain Detection with Bayesian Approximation](https://aclanthology.org/2022.coling-1.50/) (Wu et al., COLING 2022)
- Distribution Calibration for Out-of-Domain Detection with Bayesian Approximation (Wu et al., COLING 2022)
ACL
- Yanan Wu, Zhiyuan Zeng, Keqing He, Yutao Mou, Pei Wang, and Weiran Xu. 2022. Distribution Calibration for Out-of-Domain Detection with Bayesian Approximation. In Proceedings of the 29th International Conference on Computational Linguistics, pages 608–615, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.