Neuro-Symbolic Regex Synthesis Framework via Neural Example Splitting

TL;DR

This paper introduces SplitRegex, a neural-guided divide-and-conquer framework for faster and more accurate regex synthesis from string examples, outperforming previous methods on benchmark datasets.

Contribution

The paper proposes a novel neural example splitting approach and a regex synthesis framework that improves speed and accuracy over existing methods.

Findings

Significant improvement over previous regex synthesis methods.

Effective division of positive strings enhances learning accuracy.

Framework successfully handles negative string constraints.

Abstract

Due to the practical importance of regular expressions (regexes, for short), there has been a lot of research to automatically generate regexes from positive and negative string examples. We tackle the problem of learning regexes faster from positive and negative strings by relying on a novel approach called `neural example splitting'. Our approach essentially split up each example string into multiple parts using a neural network trained to group similar substrings from positive strings. This helps to learn a regex faster and, thus, more accurately since we now learn from several short-length strings. We propose an effective regex synthesis framework called `SplitRegex' that synthesizes subregexes from `split' positive substrings and produces the final regex by concatenating the synthesized subregexes. For the negative sample, we exploit pre-generated subregexes during the subregex synthesis process and perform the matching against negative strings. Then the final regex becomes consistent with all negative strings. SplitRegex is a divided-and-conquer framework for learning target regexes; split (=divide) positive strings and infer partial regexes for multiple parts, which is much more accurate than the whole string inferring, and concatenate (=conquer) inferred regexes while satisfying negative strings. We empirically demonstrate that the proposed SplitRegex framework substantially improves the previous regex synthesis approaches over four benchmark datasets.