InfeRE: Step-by-Step Regex Generation via Chain of Inference
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 t...
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| Vydané v: | IEEE/ACM International Conference on Automated Software Engineering : [proceedings] s. 1505 - 1515 |
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11.09.2023
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| 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-bystep 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. |
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| AbstractList | 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-bystep 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. |
| Author | Shen, Beijun Zhang, Shuai Gu, Xiaodong Chen, Yuting |
| Author_xml | – sequence: 1 givenname: Shuai surname: Zhang fullname: Zhang, Shuai email: zhangshuai2000@sjtu.edu.cn organization: School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University,Shanghai,China – sequence: 2 givenname: Xiaodong surname: Gu fullname: Gu, Xiaodong email: xiaodong.gu@sjtu.edu.cn organization: School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University,Shanghai,China – sequence: 3 givenname: Yuting surname: Chen fullname: Chen, Yuting email: chenyt@sjtu.edu.cn organization: School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University,Shanghai,China – sequence: 4 givenname: Beijun surname: Shen fullname: Shen, Beijun email: bjshen@sjtu.edu.cn organization: School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University,Shanghai,China |
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| Snippet | Automatically generating regular expressions (abbrev. regexes) from natural language description (NL2RE) has been an emerging research area. Prior studies... |
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| SubjectTerms | Benchmark testing Chain of Inference Codes Decoding Natural languages Regex Generation Robustness Self-Consistency Decoding Software engineering Task analysis |
| Title | InfeRE: Step-by-Step Regex Generation via Chain of Inference |
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