APP: Adaptive Prototypical Pseudo-Labeling for Few-shot OOD Detection

Pei Wang, Keqing He, Yutao Mou, Xiaoshuai Song, Yanan Wu, Jingang Wang, Yunsen Xian, Xunliang Cai, Weiran Xu


Abstract
Detecting out-of-domain (OOD) intents from user queries is essential for a task-oriented dialogue system. Previous OOD detection studies generally work on the assumption that plenty of labeled IND intents exist. In this paper, we focus on a more practical few-shot OOD setting where there are only a few labeled IND data and massive unlabeled mixed data that may belong to IND or OOD. The new scenario carries two key challenges: learning discriminative representations using limited IND data and leveraging unlabeled mixed data. Therefore, we propose an adaptive prototypical pseudo-labeling(APP) method for few-shot OOD detection, including a prototypical OOD detection framework (ProtoOOD) to facilitate low-resourceOOD detection using limited IND data, and an adaptive pseudo-labeling method to produce high-quality pseudo OOD and IND labels. Extensive experiments and analysis demonstrate the effectiveness of our method for few-shot OOD detection.
Anthology ID:
2023.findings-emnlp.258
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3926–3939
Language:
URL:
https://aclanthology.org/2023.findings-emnlp.258
DOI:
10.18653/v1/2023.findings-emnlp.258
Bibkey:
Cite (ACL):
Pei Wang, Keqing He, Yutao Mou, Xiaoshuai Song, Yanan Wu, Jingang Wang, Yunsen Xian, Xunliang Cai, and Weiran Xu. 2023. APP: Adaptive Prototypical Pseudo-Labeling for Few-shot OOD Detection. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 3926–3939, Singapore. Association for Computational Linguistics.
Cite (Informal):
APP: Adaptive Prototypical Pseudo-Labeling for Few-shot OOD Detection (Wang et al., Findings 2023)
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PDF:
https://aclanthology.org/2023.findings-emnlp.258.pdf