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

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

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 potent...

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Bibliographic Details
Main Authors: Locascio, Nicholas (Nicholas J.) (Author), Narasimhan, Karthik Rajagopal (Author), De Leon, Eduardo (Author), Kushman, Nate (Author), Barzilay, Regina (Author)
Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor)
Format: Article
Language:English
Published: Association for Computational Linguistics (ACL), 2020-12-09T22:34:33Z.
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Summary: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.

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