APP: Adaptive Prototypical Pseudo-Labeling for Few-shot OOD Detection
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866929252213981184 |
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| author | Wang, Pei He, Keqing Mou, Yutao Song, Xiaoshuai Wu, Yanan Wang, Jingang Xian, Yunsen Cai, Xunliang Xu, Weiran |
| author_facet | Wang, Pei He, Keqing Mou, Yutao Song, Xiaoshuai Wu, Yanan Wang, Jingang Xian, Yunsen Cai, Xunliang Xu, Weiran |
| contents | 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-resource OOD detection using limited IND data, and an adaptive pseudo-labeling method to produce high-quality pseudo OOD\&IND labels. Extensive experiments and analysis demonstrate the effectiveness of our method for few-shot OOD detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_13380 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | APP: Adaptive Prototypical Pseudo-Labeling for Few-shot OOD Detection Wang, Pei He, Keqing Mou, Yutao Song, Xiaoshuai Wu, Yanan Wang, Jingang Xian, Yunsen Cai, Xunliang Xu, Weiran Computation and Language 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-resource OOD detection using limited IND data, and an adaptive pseudo-labeling method to produce high-quality pseudo OOD\&IND labels. Extensive experiments and analysis demonstrate the effectiveness of our method for few-shot OOD detection. |
| title | APP: Adaptive Prototypical Pseudo-Labeling for Few-shot OOD Detection |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2310.13380 |