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

Donghyun Choi, Myeong Shin, Eunggyun Kim, Dong 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 outof-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 outof-domain samples into the training dataset could significantly improve an intent classification model's out-of-domain detection performance 1 .

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