Softregex: Generating regex from natural language descriptions using softened regex equivalence
JU Park, SK Ko, M Cognetta… - Proceedings of the 2019 …, 2019 - aclanthology.org
Proceedings of the 2019 conference on empirical methods in natural …, 2019•aclanthology.org
We continue the study of generating se-mantically correct regular expressions from natural
language descriptions (NL). The current state-of-the-art model SemRegex produces regular
expressions from NLs by rewarding the reinforced learning based on the semantic (rather
than syntactic) equivalence between two regular expressions. Since the regular expression
equivalence problem is PSPACE-complete, we introduce the EQ_Reg model for computing
the simi-larity of two regular expressions using deep neural networks. Our EQ_Reg mod-el …
language descriptions (NL). The current state-of-the-art model SemRegex produces regular
expressions from NLs by rewarding the reinforced learning based on the semantic (rather
than syntactic) equivalence between two regular expressions. Since the regular expression
equivalence problem is PSPACE-complete, we introduce the EQ_Reg model for computing
the simi-larity of two regular expressions using deep neural networks. Our EQ_Reg mod-el …
Abstract
We continue the study of generating se-mantically correct regular expressions from natural language descriptions (NL). The current state-of-the-art model SemRegex produces regular expressions from NLs by rewarding the reinforced learning based on the semantic (rather than syntactic) equivalence between two regular expressions. Since the regular expression equivalence problem is PSPACE-complete, we introduce the EQ_Reg model for computing the simi-larity of two regular expressions using deep neural networks. Our EQ_Reg mod-el essentially softens the equivalence of two regular expressions when used as a reward function. We then propose a new regex generation model, SoftRegex, us-ing the EQ_Reg model, and empirically demonstrate that SoftRegex substantially reduces the training time (by a factor of at least 3.6) and produces state-of-the-art results on three benchmark datasets.
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