Deep Unknown Intent Detection with Margin Loss

Ting-En Lin, Hua Xu


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.
Anthology ID:
P19-1548
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5491–5496
Language:
URL:
https://www.aclweb.org/anthology/P19-1548
DOI:
10.18653/v1/P19-1548
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/P19-1548.pdf