Work

Deep Unknown Intent Detection with Margin Loss

Year: 2019

Type: conference-paper

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.

Source: Annual Meeting of the Association for Computational Linguistics (ACL)

DOI: 10.18653/v1/p19-1548

Authors: Ting-En Lin, Hua Xu

Institutions: State Key Laboratory of Intelligent Technology and Systems, Beijing National Research Center for Information Science and Technology, Tsinghua University

Language: English

Topic: Speech and dialogue systems

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: National Natural Science Foundation of China

Award: G6124244177