使用基于变压器的方法检索和涉及法律信息
Mi-Young Kim1, Juliano Rabelo2, Housam Khalifa Bashier Babiker3
1Department of Science, Augustana Faculty, University of Alberta, Camrose, Alberta Canada.
概括
本研究介绍了法律信息检索和涉及任务的方法. 一个句子转换器模型改善了判例检索,而DeBERTa的大型语言模型在法规法律涉及方面获得了第三名.
科学领域:
- 法律信息学 法律信息学
- 人工智能的人工智能是人工智能.
- 自然语言处理自然语言处理.
背景情况:
- 法律领域的信息过载带来了重大挑战.
- COLIEE竞赛通过诸如判例法和法规法检索和涉及等任务来应对这些挑战.
- 开发有效的法律信息处理系统至关重要.
研究的目的:
- 描述COLIEE 2023竞赛中的任务1 (案例法检索) 和任务4 (法定法律涉及) 的方法.
- 评估不同自然语言处理模型对法律任务的性能.
主要方法:
- 在任务1中,句子转换器模型生成了案例段落的数值表示,并使用相似性直方图来构建二进制分类器.
- 对于任务4,一个预训练的DeBERTa大语言模型 (LLM) 在SNLI和MultiNLI数据集上进行了微调.
- 性能与知识蒸和整体方法 (随机森林,投票) 相比较.
主要成果:
- 句子转换器方法为判例检索提供了一种方法.
- 基于DeBERTa LLM的第4项任务方法在COLIEE 2023竞赛中在八支团队中获得了第三名.
- 对DeBERTa与其他模型进行了比较分析.
结论:
- 开发的方法在解决法律信息过载方面表现有前途.
- 像DeBERTa这样的微调LLM对于法律涉及任务是有效的.
- 进一步的研究可以建立在这些方法上,以加强法律信息系统.
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