# Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge

arXiv: https://arxiv.org/abs/1608.03000 · HTML: https://mlsanity.com/paper/1608.03000/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2016.

## Built on by (most cited first, 24 of 24)

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## Abstract (arXiv)

This paper explores the task of translating natural language queries into regular expressions which embody their meaning. In contrast to prior work, the proposed neural model does not utilize domain-specific crafting, learning to translate directly from a parallel corpus. To fully explore the potential of neural models, we propose a methodology for collecting a large corpus of regular expression, natural language pairs. Our resulting model achieves a performance gain of 19.6% over previous state-of-the-art models.