@inproceedings{lin-xu-2019-deep,
title = "Deep Unknown Intent Detection with Margin Loss",
author = "Lin, Ting-En and
Xu, Hua",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1548",
doi = "10.18653/v1/P19-1548",
pages = "5491--5496",
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.",
}
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%0 Conference Proceedings
%T Deep Unknown Intent Detection with Margin Loss
%A Lin, Ting-En
%A Xu, Hua
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F lin-xu-2019-deep
%X 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.
%R 10.18653/v1/P19-1548
%U https://aclanthology.org/P19-1548
%U https://doi.org/10.18653/v1/P19-1548
%P 5491-5496
Markdown (Informal)
[Deep Unknown Intent Detection with Margin Loss](https://aclanthology.org/P19-1548) (Lin & Xu, ACL 2019)
ACL
- Ting-En Lin and Hua Xu. 2019. Deep Unknown Intent Detection with Margin Loss. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5491–5496, Florence, Italy. Association for Computational Linguistics.