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128768 |
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|a dc
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|a Locascio, Nicholas
|q (Nicholas J.)
|e author
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|a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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|a Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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|a Narasimhan, Karthik Rajagopal
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|a De Leon, Eduardo
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|a Kushman, Nate
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|a Barzilay, Regina
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|a Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge
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|b Association for Computational Linguistics (ACL),
|c 2020-12-09T22:34:33Z.
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|z Get fulltext
|u https://hdl.handle.net/1721.1/128768
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|a 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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|a en
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|a Article
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|t Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
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