# Marrying up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding

arXiv: https://arxiv.org/abs/1805.05588 · HTML: https://mlsanity.com/paper/1805.05588/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2018.

## Built on by (most cited first, 7 of 7)

- [Linguistically-Enriched and Context-Aware Zero-shot Slot Filling](https://mlsanity.com/paper/2101.06514/) (2021)
- [HERALD: An Annotation Efficient Method to Detect User Disengagement in Social Conversations](https://mlsanity.com/paper/2106.00162/) (2021)
- [Exploring Distantly-Labeled Rationales in Neural Network Models](https://mlsanity.com/paper/2106.01809/) (2021)
- [Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling](https://mlsanity.com/paper/2106.07343/) (2021)
- [InfeRE: Step-by-Step Regex Generation via Chain of Inference](https://mlsanity.com/paper/2308.04041/) (2023)
- [Toward Open-domain Slot Filling via Self-supervised Co-training](https://mlsanity.com/paper/2303.13801/) (2023)
- [Integrating Regular Expressions with Neural Networks via DFA](https://mlsanity.com/paper/2109.02882/) (2021)

## Abstract (arXiv)

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.