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
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)
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PDF:
https://aclanthology.org/2022.coling-1.36.pdf