TY - GEN
T1 - Neural generation of regular expressions from natural language with minimal domain knowledge
AU - Locascio, Nicholas
AU - Narasimhan, Karthik
AU - DeLeon, Eduardo
AU - Kushman, Nate
AU - Barzilay, Regina
PY - 2016/1/1
Y1 - 2016/1/1
N2 - 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 corpus1 of regular expression, natural language pairs. Our resulting model achieves a performance gain of 19.6% over previous state-of-the-art models.
AB - 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 corpus1 of regular expression, natural language pairs. Our resulting model achieves a performance gain of 19.6% over previous state-of-the-art models.
UR - http://www.scopus.com/inward/record.url?scp=85072820197&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85072820197&partnerID=8YFLogxK
M3 - Conference contribution
T3 - EMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings
SP - 1918
EP - 1923
BT - EMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings
PB - Association for Computational Linguistics (ACL)
T2 - 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016
Y2 - 1 November 2016 through 5 November 2016
ER -