APP: Adaptive Prototypical Pseudo-Labeling for Few-shot OOD Detection
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-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.
Cite
@article{arxiv.2310.13380,
title = {APP: Adaptive Prototypical Pseudo-Labeling for Few-shot OOD Detection},
author = {Pei Wang and Keqing He and Yutao Mou and Xiaoshuai Song and Yanan Wu and Jingang Wang and Yunsen Xian and Xunliang Cai and Weiran Xu},
journal= {arXiv preprint arXiv:2310.13380},
year = {2024}
}