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
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.