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arXiv · 2105.05601

OutFlip: Generating Out-of-Domain Samples for Unknown Intent Detection with Natural Language Attack

DongHyun Choi·Myeong Cheol Shin·EungGyun Kim·Dong Ryeol Shin
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Abstract

Out-of-domain (OOD) input detection is vital in a task-oriented dialogue system since the acceptance of unsupported inputs could lead to an incorrect response of the system. This paper proposes OutFlip, a method to generate out-of-domain samples using only in-domain training dataset automatically. A white-box natural language attack method HotFlip is revised to generate out-of-domain samples instead of adversarial examples. Our evaluation results showed that integrating OutFlip-generated out-of-domain samples into the training dataset could significantly improve an intent classification model's out-of-domain detection performance.

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DongHyun Choi, Myeong Cheol Shin, EungGyun Kim, Dong Ryeol Shin. 2021-05-12. OutFlip: Generating Out-of-Domain Samples for Unknown Intent Detection with Natural Language Attack. https://arxiv.org/abs/2105.05601

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