@inproceedings{luo-etal-2018-marrying,
title = "Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding",
author = "Luo, Bingfeng and
Feng, Yansong and
Wang, Zheng and
Huang, Songfang and
Yan, Rui and
Zhao, Dongyan",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-1194",
doi = "10.18653/v1/P18-1194",
pages = "2083--2093",
abstract = "The success of many natural language processing (NLP) tasks is bound by the number and quality of annotated data, but there is often a shortage of such training data. In this paper, we ask the question: {``}Can we combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP?{''}. In answer, we develop novel methods to exploit the rich expressiveness of REs at different levels within a NN, showing that the combination significantly enhances the learning effectiveness when a small number of training examples are available. We evaluate our approach by applying it to spoken language understanding for intent detection and slot filling. Experimental results show that our approach is highly effective in exploiting the available training data, giving a clear boost to the RE-unaware NN.",
}
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<abstract>The success of many natural language processing (NLP) tasks is bound by the number and quality of annotated data, but there is often a shortage of such training data. In this paper, we ask the question: “Can we combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP?”. In answer, we develop novel methods to exploit the rich expressiveness of REs at different levels within a NN, showing that the combination significantly enhances the learning effectiveness when a small number of training examples are available. We evaluate our approach by applying it to spoken language understanding for intent detection and slot filling. Experimental results show that our approach is highly effective in exploiting the available training data, giving a clear boost to the RE-unaware NN.</abstract>
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%0 Conference Proceedings
%T Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding
%A Luo, Bingfeng
%A Feng, Yansong
%A Wang, Zheng
%A Huang, Songfang
%A Yan, Rui
%A Zhao, Dongyan
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F luo-etal-2018-marrying
%X The success of many natural language processing (NLP) tasks is bound by the number and quality of annotated data, but there is often a shortage of such training data. In this paper, we ask the question: “Can we combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP?”. In answer, we develop novel methods to exploit the rich expressiveness of REs at different levels within a NN, showing that the combination significantly enhances the learning effectiveness when a small number of training examples are available. We evaluate our approach by applying it to spoken language understanding for intent detection and slot filling. Experimental results show that our approach is highly effective in exploiting the available training data, giving a clear boost to the RE-unaware NN.
%R 10.18653/v1/P18-1194
%U https://aclanthology.org/P18-1194
%U https://doi.org/10.18653/v1/P18-1194
%P 2083-2093
Markdown (Informal)
[Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding](https://aclanthology.org/P18-1194) (Luo et al., ACL 2018)
ACL