Adversarial Generative Distance-Based Classifier for Robust Out-of-Domain Detection

Zhiyuan Zeng; Hong Xu; Keqing He; Yuanmeng Yan; Sihong Liu; Zijun Liu; Weiran Xu

ICASSP 2021accepted0 citations

Adversarial Generative Distance-Based Classifier for Robust Out-of-Domain Detection

Zhiyuan Zeng, Hong Xu, Keqing He, Yuanmeng Yan, Sihong Liu, Zijun Liu, Weiran Xu

Abstract

Detecting out-of-domain (OOD) intents is critical in a task-oriented dialog system. Existing methods rely heavily on extensive manually labeled OOD samples and lack robustness. In this paper, we propose an efficient adversarial attack mechanism to augment hard OOD samples and design a novel generative distance-based classifier to detect OOD samples instead of a traditional threshold-based discriminator classifier. Experiments on two public benchmark datasets show that our method can consistently outperform the baselines with a statistically significant margin.

BibTeX

@inproceedings{icassp2021_adversarialgener,
  title = {Adversarial Generative Distance-Based Classifier for Robust Out-of-Domain Detection},
  author = {Zhiyuan Zeng and Hong Xu and Keqing He and Yuanmeng Yan and Sihong Liu and Zijun Liu and Weiran Xu},
  booktitle = {ICASSP 2021},
  year = {2021}
}