A Closer Look at Few-Shot Out-of-Distribution Intent Detection
Li-Ming Zhan, Haowen Liang, Lu Fan, Xiao-Ming Wu, Albert Y.S. Lam
Abstract
We consider few-shot out-of-distribution (OOD) intent detection, a practical and important problem for the development of task-oriented dialogue systems. Despite its importance, this problem is seldom studied in the literature, let alone examined in a systematic way. In this work, we take a closer look at this problem and identify key issues for research. In our pilot study, we reveal the reason why existing OOD intent detection methods are not adequate in dealing with this problem. Based on the observation, we propose a promising approach to tackle this problem based on latent representation generation and self-supervision. Comprehensive experiments on three real-world intent detection benchmark datasets demonstrate the high effectiveness of our proposed approach and its great potential in improving state-of-the-art methods for few-shot OOD intent detection.- Anthology ID:
- 2022.coling-1.36
- 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:
- 451–460
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.36
- DOI:
- Bibkey:
- Cite (ACL):
- Li-Ming Zhan, Haowen Liang, Lu Fan, Xiao-Ming Wu, and Albert Y.S. Lam. 2022. A Closer Look at Few-Shot Out-of-Distribution Intent Detection. In Proceedings of the 29th International Conference on Computational Linguistics, pages 451–460, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- A Closer Look at Few-Shot Out-of-Distribution Intent Detection (Zhan et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.36.pdf
- Code
- liam0949/few-shot-intent-ood
Export citation
@inproceedings{zhan-etal-2022-closer,
title = "A Closer Look at Few-Shot Out-of-Distribution Intent Detection",
author = "Zhan, Li-Ming and
Liang, Haowen and
Fan, Lu and
Wu, Xiao-Ming and
Lam, Albert Y.S.",
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-1.36",
pages = "451--460",
abstract = "We consider few-shot out-of-distribution (OOD) intent detection, a practical and important problem for the development of task-oriented dialogue systems. Despite its importance, this problem is seldom studied in the literature, let alone examined in a systematic way. In this work, we take a closer look at this problem and identify key issues for research. In our pilot study, we reveal the reason why existing OOD intent detection methods are not adequate in dealing with this problem. Based on the observation, we propose a promising approach to tackle this problem based on latent representation generation and self-supervision. Comprehensive experiments on three real-world intent detection benchmark datasets demonstrate the high effectiveness of our proposed approach and its great potential in improving state-of-the-art methods for few-shot OOD intent detection.",
}
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%0 Conference Proceedings %T A Closer Look at Few-Shot Out-of-Distribution Intent Detection %A Zhan, Li-Ming %A Liang, Haowen %A Fan, Lu %A Wu, Xiao-Ming %A Lam, Albert Y.S. %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 zhan-etal-2022-closer %X We consider few-shot out-of-distribution (OOD) intent detection, a practical and important problem for the development of task-oriented dialogue systems. Despite its importance, this problem is seldom studied in the literature, let alone examined in a systematic way. In this work, we take a closer look at this problem and identify key issues for research. In our pilot study, we reveal the reason why existing OOD intent detection methods are not adequate in dealing with this problem. Based on the observation, we propose a promising approach to tackle this problem based on latent representation generation and self-supervision. Comprehensive experiments on three real-world intent detection benchmark datasets demonstrate the high effectiveness of our proposed approach and its great potential in improving state-of-the-art methods for few-shot OOD intent detection. %U https://aclanthology.org/2022.coling-1.36 %P 451-460
Markdown (Informal)
[A Closer Look at Few-Shot Out-of-Distribution Intent Detection](https://aclanthology.org/2022.coling-1.36) (Zhan et al., COLING 2022)
- A Closer Look at Few-Shot Out-of-Distribution Intent Detection (Zhan et al., COLING 2022)
ACL
- Li-Ming Zhan, Haowen Liang, Lu Fan, Xiao-Ming Wu, and Albert Y.S. Lam. 2022. A Closer Look at Few-Shot Out-of-Distribution Intent Detection. In Proceedings of the 29th International Conference on Computational Linguistics, pages 451–460, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.