Out-of-Scope Intent Detection with Supervised Deep Metric Learning
Youwen Zhang, Xudong Wang, Linlin Wang, Ke Yan, Huan Chen · 2023
Detecting Out-of-Scope(OOS) intents in dialogue systems is a challenging technique with practical applications. As for OOS intent detection, it not only ensures the accuracy of classifying known intents but detecting OOS intents is also crucial. Current related models are limited in learning decision boundaries or setting the threshold of confidence score, which all neglect that a well-formed intent representation is a key point. Meanwhile, text extractors trained by traditional cross-entropy loss merely focus on reducing the error rate of the class to which the sample is classified. In this paper, we propose an effective feature extraction method based on deep metric learning to construct the triplet network with prior knowledge. With the constructed triplet loss, mining hard samples, which refers to the far-apart intents between the same class and close intent representations among different classes, can further obtain discriminative intent representations. In addition, we also introduce adversarial training to make intent representations more robust. Experiments on three public datasets prove the effectiveness of our proposed method of learning discriminative intent representations.