@inproceedings{mao-etal-2023-enhancing,
title = "Enhancing Language Model with Unit Test Techniques for Efficient Regular Expression Generation",
author = "Mao, Chenhui and
Lin, Xiexiong and
Jin, Xin and
Zhang, Xin",
editor = "Wang, Mingxuan and
Zitouni, Imed",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-industry.2",
doi = "10.18653/v1/2023.emnlp-industry.2",
pages = "12--19",
abstract = "Recent research has investigated the use of generative language models to produce regular expressions with semantic-based approaches. However, these approaches have shown shortcomings in practical applications, particularly in terms of functional correctness, which refers to the ability to reproduce the intended function inputs by the user. To address this issue, we present a novel method called Unit-Test Driven Reinforcement Learning (UTD-RL). Our approach differs from previous methods by taking into account the crucial aspect of functional correctness and transforming it into a differentiable gradient feedback using policy gradient techniques. In which functional correctness can be evaluated through Unit Tests, a testing method that ensures regular expressions meets its design and performs as intended. Experiments conducted on three public datasets demonstrate the effectiveness of the proposed method in generating regular expressions. This method has been employed in a regulatory scenario where regular expressions can be utilized to ensure that all online content is free from non-compliant elements, thereby significantly reducing the workload of relevant personnel.",
}
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%0 Conference Proceedings
%T Enhancing Language Model with Unit Test Techniques for Efficient Regular Expression Generation
%A Mao, Chenhui
%A Lin, Xiexiong
%A Jin, Xin
%A Zhang, Xin
%Y Wang, Mingxuan
%Y Zitouni, Imed
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F mao-etal-2023-enhancing
%X Recent research has investigated the use of generative language models to produce regular expressions with semantic-based approaches. However, these approaches have shown shortcomings in practical applications, particularly in terms of functional correctness, which refers to the ability to reproduce the intended function inputs by the user. To address this issue, we present a novel method called Unit-Test Driven Reinforcement Learning (UTD-RL). Our approach differs from previous methods by taking into account the crucial aspect of functional correctness and transforming it into a differentiable gradient feedback using policy gradient techniques. In which functional correctness can be evaluated through Unit Tests, a testing method that ensures regular expressions meets its design and performs as intended. Experiments conducted on three public datasets demonstrate the effectiveness of the proposed method in generating regular expressions. This method has been employed in a regulatory scenario where regular expressions can be utilized to ensure that all online content is free from non-compliant elements, thereby significantly reducing the workload of relevant personnel.
%R 10.18653/v1/2023.emnlp-industry.2
%U https://aclanthology.org/2023.emnlp-industry.2
%U https://doi.org/10.18653/v1/2023.emnlp-industry.2
%P 12-19
Markdown (Informal)
[Enhancing Language Model with Unit Test Techniques for Efficient Regular Expression Generation](https://aclanthology.org/2023.emnlp-industry.2) (Mao et al., EMNLP 2023)
ACL