Neural generation of regular expressions from natural language with minimal domain knowledge
N Locascio, K Narasimhan, E DeLeon… - arXiv preprint arXiv …, 2016 - arxiv.org
arXiv preprint arXiv:1608.03000, 2016•arxiv.org
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.
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.
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.
arxiv.org