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arXiv · 2210.10722

UniNL: Aligning Representation Learning with Scoring Function for OOD Detection via Unified Neighborhood Learning

Yutao Mou·Pei Wang·Keqing He·Yanan Wu·Jingang Wang·Wei Wu·Weiran Xu
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Abstract

Detecting out-of-domain (OOD) intents from user queries is essential for avoiding wrong operations in task-oriented dialogue systems. The key challenge is how to distinguish in-domain (IND) and OOD intents. Previous methods ignore the alignment between representation learning and scoring function, limiting the OOD detection performance. In this paper, we propose a unified neighborhood learning framework (UniNL) to detect OOD intents. Specifically, we design a K-nearest neighbor contrastive learning (KNCL) objective for representation learning and introduce a KNN-based scoring function for OOD detection. We aim to align representation learning with scoring function. Experiments and analysis on two benchmark datasets show the effectiveness of our method.

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Yutao Mou, Pei Wang, Keqing He, Yanan Wu, Jingang Wang, Wei Wu, Weiran Xu. 2022-10-19. UniNL: Aligning Representation Learning with Scoring Function for OOD Detection via Unified Neighborhood Learning. https://arxiv.org/abs/2210.10722

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