InfeRE: Step-by-Step Regex Generation via Chain of Inference
Shuai Zhang Xiaodong Gu Yuting Chen Beijun Shen
Automatically generating regular expressions (abbrev. regexes) from natural
language description (NL2RE) has been an emerging research area. Prior studies
treat regex as a linear sequence of tokens and generate the final expressions
autoregressively in a single pass. They did not take into account the
step-by-step internal text-matching processes behind the final results. This
significantly hinders the efficacy and interpretability of regex generation by
neural language models. In this paper, we propose a new paradigm called InfeRE,
which decomposes the generation of regexes into chains of step-by-step
inference. To enhance the robustness, we introduce a self-consistency decoding
mechanism that ensembles multiple outputs sampled from different models. We
evaluate InfeRE on two publicly available datasets, NL-RX-Turk and KB13, and
compare the results with state-of-the-art approaches and the popular tree-based
generation approach TRANX. Experimental results show that InfeRE substantially
outperforms previous baselines, yielding 16.3% and 14.7% improvement in DFA@5
accuracy on two datasets, respectively. Particularly, InfeRE outperforms the
popular tree-based generation approach by 18.1% and 11.3% on both datasets,
respectively, in terms of DFA@5 accuracy.