Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge
Nicholas Locascio, Karthik Narasimhan, Eduardo DeLeon, Nate Kushman, Regina Barzilay · 2016
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 1 of regular expression, natural language pairs.Our resulting model achieves a performance gain of 19.6% over previous state-of-the-art models.