增强BiGRU与KAN模块在法律文档分类与摘要中的应用
Enhancing BiGRU with a KAN Block for Legal Document Classification and Summarization
AI总结：
提出一种基于KAN的BiGRU模型，用于低资源多语言法律文档的分类与摘要，通过KAN模块提升分类准确率至67.96%。
摘要：
本研究引入了一种基于KAN的BiGRU模型的新架构，用于低资源多语言环境下的法律文档分类与摘要任务。为了解决领域语言、不同语言使用、上下文长依赖和类别不平衡等问题，我们使用了由孟加拉国法律文档组成的数据集，这些文档来自Manupatra，包括孟加拉语、英语和音译孟加拉语。我们的分类任务采用BiGRU模型以及Kolmogorov-Arnold网络（KAN）模块，而摘要部分则利用基于注意力的GRU结合KAN模型头部。分类模型达到了67.96%的准确率和0.65的F1分数；摘要的ROUGE-1、ROUGE-2和ROUGE-L指标分别对应0.38、0.23和0.31的F1分数。消融研究表明，使用KAN将分类准确率从57.34%提升至67.96%。此外，我们将所提出的技术与多个基线进行了比较，包括经典机器学习算法和预训练语言模型。
英文摘要：
This study introduces a novel architecture of KAN-based BiGRU model for the task of classification and summarization of legal documents in a low-resource multilingual setup. In order to tackle problems associated with domain language, the usage of different languages, long dependencies within context, and class imbalance, we employ the dataset composed of legal documents from Bangladesh and taken from Manupatra, which include Bengali, English, and transliterated Bengali languages. Our classification task involves BiGRU model, along with Kolmogorov-Arnold Network (KAN) module, while the summarization part utilizes attention-based GRU, combined with a KAN model head. Classification model yields 67.96% of accuracy and 0.65 F1 score; while ROUGE-1, ROUGE-2, and ROUGE-L measures for summarization yield 0.38, 0.23, and 0.31 F1 scores, correspondingly. Ablation study shows that the use of KAN increases classification accuracy from 57.34% to 67.96%. Moreover, our proposed technique is compared to several baselines, including classical ML algorithms and pretrained language models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
