@inproceedings{wang-etal-2023-app,
title = "{APP}: Adaptive Prototypical Pseudo-Labeling for Few-shot {OOD} Detection",
author = "Wang, Pei and
He, Keqing and
Mou, Yutao and
Song, Xiaoshuai and
Wu, Yanan and
Wang, Jingang and
Xian, Yunsen and
Cai, Xunliang and
Xu, Weiran",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.258",
doi = "10.18653/v1/2023.findings-emnlp.258",
pages = "3926--3939",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T APP: Adaptive Prototypical Pseudo-Labeling for Few-shot OOD Detection
%A Wang, Pei
%A He, Keqing
%A Mou, Yutao
%A Song, Xiaoshuai
%A Wu, Yanan
%A Wang, Jingang
%A Xian, Yunsen
%A Cai, Xunliang
%A Xu, Weiran
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F wang-etal-2023-app
%X 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.
%R 10.18653/v1/2023.findings-emnlp.258
%U https://aclanthology.org/2023.findings-emnlp.258
%U https://doi.org/10.18653/v1/2023.findings-emnlp.258
%P 3926-3939
Markdown (Informal)
[APP: Adaptive Prototypical Pseudo-Labeling for Few-shot OOD Detection](https://aclanthology.org/2023.findings-emnlp.258) (Wang et al., Findings 2023)
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.