Distribution Calibration for Out-of-Domain Detection with Bayesian Approximation

• URL: http://arxiv.org/abs/2209.06612v1
• Date: Wed, 14 Sep 2022 13:04:09 GMT
• Title: Distribution Calibration for Out-of-Domain Detection with Bayesian Approximation
• Authors: Yanan Wu, Zhiyuan Zeng, Keqing He, Yutao Mou, Pei Wang, Weiran Xu
• Abstract summary: Out-of-Domain (OOD) detection is a key component in a task-oriented dialog system. Previous softmax-based detection algorithms are proved to be overconfident for OOD samples. We propose a Bayesian OOD detection framework to calibrate distribution uncertainty using Monte-Carlo Dropout.
• Score: 35.34001858858684
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Out-of-Domain (OOD) detection is a key component in a task-oriented dialog system, which aims to identify whether a query falls outside the predefined supported intent set. Previous softmax-based detection algorithms are proved to be overconfident for OOD samples. In this paper, we analyze overconfident OOD comes from distribution uncertainty due to the mismatch between the training and test distributions, which makes the model can't confidently make predictions thus probably causing abnormal softmax scores. We propose a Bayesian OOD detection framework to calibrate distribution uncertainty using Monte-Carlo Dropout. Our method is flexible and easily pluggable into existing softmax-based baselines and gains 33.33\% OOD F1 improvements with increasing only 0.41\% inference time compared to MSP. Further analyses show the effectiveness of Bayesian learning for OOD detection.

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