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.Comment: This paper has been accepted by ASE'2