Deep Unknown Intent Detection with Margin Loss

By Ting-En Lin, Hua Xu. 2019

164 citations recorded by the source corpus.

Identifying the unknown (novel) user intents that have never appeared in the training set is a challenging task in the dialogue system. In this paper, we present a two-stage method for detecting unknown intents. We use bidirectional long short-term memory (BiLSTM) network with the margin loss as the feature extractor. With margin loss, we can learn discriminative deep features by forcing the network to maximize inter-class variance and to minimize intra-class variance. Then, we feed the feature vectors to the density-based novelty detection algorithm, local outlier factor (LOF), to detect unknown intents. Experiments on two benchmark datasets show that our method can yield consistent improvements compared with the baseline methods.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology

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Citations in the corpus

An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction (2019, methodology): “Similar experiments were conducted for evaluating unknown intent discovery models in Lin and Xu (2019).”
Unknown Intent Detection Using Gaussian Mixture Model with an Application to Zero-shot Intent Classification (2020, methodology): “For MSP, we set the threshold as 0.5 following Lin and Xu (2019).”