Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge
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1608.03000.pdf
Description
Submitted version
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327.87 KB
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Author(s)
Locascio, Nicholas (Nicholas J.)
Narasimhan, Karthik Rajagopal
De Leon, Eduardo
Kushman, Nate
Barzilay, Regina
Date Issued
November 2016
Journal
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
Publisher
Association for Computational Linguistics (ACL)
Citation
Locascio, Nicholas et al. "Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge." Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, November 2016, Austin, Texas, Association for Computational Linguistics, November 2016. © 2016 Association for Computational Linguistics
Version
Original manuscript
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.18653/v1/d16-1197
