Papers with regex generation model

    1 papers
    SoftRegex: Generating Regex from Natural Language Descriptions using Softened Regex Equivalence (D19-1)

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    Challenge: Existing models that generate semantically correct regular expressions from NLs are not yet fully understood.
    Approach: They propose a model that rewards reinforcement learning based on the semantic equivalence between two regular expressions.
    Outcome: The proposed model reduces training time and produces state-of-the-art results on three benchmark datasets.

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