Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding
Bingfeng Luo, Yansong Feng, Zheng Wang, Songfang Huang, Rui Yan, Dongyan Zhao
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.- Anthology ID:
- P18-1194
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2083–2093
- Language:
- URL:
- https://aclanthology.org/P18-1194
- DOI:
- 10.18653/v1/P18-1194
- Cite (ACL):
- Bingfeng Luo, Yansong Feng, Zheng Wang, Songfang Huang, Rui Yan, and Dongyan Zhao. 2018. Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2083–2093, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding (Luo et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/P18-1194.pdf
- Data
- ATIS