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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 3

Legal text reading comprehension model based on multi-granularity enhancement and answer verification

Wang Jinglei
Xu Jian
School of Computer Science & Engineering, Nanjing University of Science & Technology, Nanjing Jiangsu 210094, China
DOI: 10.19734/j.issn.1001-3695.2024.09.0314

Abstract

In recent years, legal text reading comprehension has gradually become a research hotspot, requiring models to complete answer classification and extraction of evidence and answers with limited data. However, the existing models encode only at a single granularity and still lack sufficient interaction between questions and legal texts. In order to solve these problems, this paper proposed a legal text reading comprehension model based on multi-granularity enhancement and answer verification. The model firstly used the multi-granularity encoding module to capture multi-level information from the input texts at three granularities: character, word and sequence, which improved the model’s ability to extract information. Then, it introduced an attention based on questions and evidence to enhance the interaction between questions and legal texts and to deeply mine the clues for answer classification. Finally, it proposed an answer verification mechanism inspired by the human reading process to predict answers by integrating local and global information. Experimental results on the Chinese legal reading comprehension datasets CAIL2019, CAIL2020, CAIL2020-Enhanced and the English dataset HotpotQA, show that the proposed method achieves 76.48%, 64.16%, 70.82% and 69.39% joint F1 respectively, outperforming the baseline models.

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.09.0314
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 3

Publish History

[2024-12-16] Accepted Paper

Cite This Article

王婧蕾, 徐建. 基于多粒度增强和答案验证的法律文书阅读理解模型 [J]. 计算机应用研究, 2025, 42 (3). (2024-12-16). https://doi.org/10.19734/j.issn.1001-3695.2024.09.0314. (Wang Jinglei, Xu Jian. Legal text reading comprehension model based on multi-granularity enhancement and answer verification [J]. Application Research of Computers, 2025, 42 (3). (2024-12-16). https://doi.org/10.19734/j.issn.1001-3695.2024.09.0314. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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