Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge
Computation and Language
2016-08-11 v1 Artificial Intelligence
Abstract
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.
Keywords
Cite
@article{arxiv.1608.03000,
title = {Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge},
author = {Nicholas Locascio and Karthik Narasimhan and Eduardo DeLeon and Nate Kushman and Regina Barzilay},
journal= {arXiv preprint arXiv:1608.03000},
year = {2016}
}
Comments
to be published in EMNLP 2016