Abstract

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.

Highlights

  • It is essential to identify the unknown intents that have never appeared in the training set

  • We can see that the deep features learned with large margin cosine loss (LMCL) are intra-class com

  • We proposed a two-stage method for unknown intent detection

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Summary

Introduction

It is essential to identify the unknown intents that have never appeared in the training set. We can use those unknown intents to discover potential business opportunities. It can provide guidance for developers and accelerate the system development process. It is often difficult to obtain prior knowledge about unknown intents due to lack of examples. It is hard to estimate the exact number of unknown intents. Since user intents are strongly guided by prior knowledge and context, modeling high-level semantic concepts of intent is still problematic

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