We continue the study of generating semanti-cally correct regular expressions from natural language descriptions (XL). 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 PSPACK-complete, we introduce the EQ_Reg model for computing the similarity of two regular expressions using deep neural networks. Our EQ_Reg model essentially softens the equivalence of two regular expressions when used as a reward function. We then propose a new regex generation model, SoftRegex, using 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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