  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.
