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
- 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)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.findings-emnlp.258.pdf