Abstract: Regular expressions are composed of a series of characters and metacharacters,defining a matching pattern that can be used to check whether a string matches the desired criteria.Many developers find it is difficlult to write regular expressions during the software development process.Therefore,generating regular expressions based on natural language requirements has become a research focus.In recent years,systems that transform natural language descriptions into regular expressions have achieved some research results,but often only for simple serialized texts.This paper explores methods for converting natural language queries into regular expressions that can execute their intended functionality.Given the successful application of syntactic parsing in natural language processing,our model utilizes the structural information of natural language by embedding syntax parse trees in a hierarchically aggregated manner.We employ the Tree-transformer architecture,suitable for input tree structures,to perform self-attention encoding on natural language descriptions.The decoder uses cross-attention to predict the regular expression.The model is validated on two public datasets.Experimental results demonstrate that our model effectively improves the quality of generated regular expressions.It outperforms existing models in the DFA-Equal-Acc evaluation metric.

Key words: Regular expression generation, Tree-Transformer, Syntactic parsing