InfeRE: Step-by-Step Regex Generation via Chain of Inference

• URL: http://arxiv.org/abs/2308.04041v1
• Date: Tue, 8 Aug 2023 04:37:41 GMT
• Title: InfeRE: Step-by-Step Regex Generation via Chain of Inference
• Authors: Shuai Zhang, Xiaodong Gu, Yuting Chen, Beijun Shen
• Abstract summary: In this paper, we propose a new paradigm called InfeRE, which decomposes the generation of expressions into chains of step-by-step inference. 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.
• Score: 15.276963928784047
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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