@inproceedings{williams-2019-neural,
title = "Neural Lexicons for Slot Tagging in Spoken Language Understanding",
author = "Williams, Kyle",
editor = "Loukina, Anastassia and
Morales, Michelle and
Kumar, Rohit",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-2011",
doi = "10.18653/v1/N19-2011",
pages = "83--89",
abstract = "We explore the use of lexicons or gazettes in neural models for slot tagging in spoken language understanding. We develop models that encode lexicon information as neural features for use in a Long-short term memory neural network. Experiments are performed on data from 4 domains from an intelligent assistant under conditions that often occur in an industry setting, where there may be: 1) large amounts of training data, 2) limited amounts of training data for new domains, and 3) cross domain training. Results show that the use of neural lexicon information leads to a significant improvement in slot tagging, with improvements in the F-score of up to 12{\%}. Our findings have implications for how lexicons can be used to improve the performance of neural slot tagging models.",
}
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%0 Conference Proceedings
%T Neural Lexicons for Slot Tagging in Spoken Language Understanding
%A Williams, Kyle
%Y Loukina, Anastassia
%Y Morales, Michelle
%Y Kumar, Rohit
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F williams-2019-neural
%X We explore the use of lexicons or gazettes in neural models for slot tagging in spoken language understanding. We develop models that encode lexicon information as neural features for use in a Long-short term memory neural network. Experiments are performed on data from 4 domains from an intelligent assistant under conditions that often occur in an industry setting, where there may be: 1) large amounts of training data, 2) limited amounts of training data for new domains, and 3) cross domain training. Results show that the use of neural lexicon information leads to a significant improvement in slot tagging, with improvements in the F-score of up to 12%. Our findings have implications for how lexicons can be used to improve the performance of neural slot tagging models.
%R 10.18653/v1/N19-2011
%U https://aclanthology.org/N19-2011
%U https://doi.org/10.18653/v1/N19-2011
%P 83-89
Markdown (Informal)
[Neural Lexicons for Slot Tagging in Spoken Language Understanding](https://aclanthology.org/N19-2011) (Williams, NAACL 2019)
ACL
- Kyle Williams. 2019. Neural Lexicons for Slot Tagging in Spoken Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers), pages 83–89, Minneapolis, Minnesota. Association for Computational Linguistics.