Out-of-scope intent detection with intent-invariant data augmentation

Abstract

In practical dialogue systems, it is crucial to avoid undesired responses and poor user experiences by detecting Out-Of-Scope (OOS) intents from user utterances. Currently, to detect OOS intents, limited-supervised methods are more potential due to using limited true OOS data to better model the OOS data distribution. However, existing limited-supervised methods often yield low-quality OOS augmentations, i.e., noisy OOS and long-distant OOS, due to the use of the retrieval-based data augmentation mechanism, which will damage the OOS intent detection performance. To tackle this problem, in this paper, we propose a novel OOS intent detection method by leveraging intent-invariant data augmentation, called InInOOS, which can generate high-quality pseudo-OOS utterances with invariant OOS intents but various slot values and different expressions to further enhance the OOS intent detection performance. Specifically, we first generate pseudo-OOS candidates using our pre-trained intent-invariant utterance generation model, and then the most beneficial candidates are selected to train an OOS intent detection model. Extensive experiments on two public datasets show that our method performs better than state-of-the-art baselines.