Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge
Author(s)Locascio, Nicholas (Nicholas J.); Narasimhan, Karthik Rajagopal; De Leon, Eduardo; Kushman, Nate; Barzilay, Regina
MetadataShow full item record
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.
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
Association for Computational Linguistics (ACL)
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