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

2022-10-19 17:06:34
Yutao Mou, Pei Wang, Keqing He, Yanan Wu, Jingang Wang, Wei Wu, Weiran Xu
arXiv_CL
             
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.

Abstract (translated)

URL

https://arxiv.org/abs/2210.10722

PDF

https://arxiv.org/pdf/2210.10722.pdf

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