结合句子位置嵌入增强预训练语言模型以用于法律意见书修辞角色识别
Enhancing Pre-Trained Language Models with Sentence Position Embeddings for Rhetorical Roles Recognition in Legal Opinions
AI总结：
本研究针对法律意见书修辞角色识别难题，提出融合句子位置信息的预训练语言模型架构，在LegalEval@SemEval2023语料上以更低参数和计算成本实现了优异性能，验证了位置信息与注意力机制的提升作用。
摘要：
法律领域是一个庞大且复杂的领域，涉及大量文本分析工作，涵盖法律条文、法律论证与法律意见书等内容。法律从业者必须分析这些文本以理解法律案件、研究法律先例并准备法律文书。法律意见书的规模持续增长，鉴于其复杂性与多样性，开发能够准确预测法律意见书修辞角色的模型难度日益提升。\n在本研究论文中，我们提出了一种新型模型架构，利用融合了文档内句子位置信息知识的预训练语言模型（PLMs）自动预测修辞角色。基于LegalEval@SemEval2023竞赛的标注语料库，我们证明：与在全局上下文中采用分层模型的复杂架构相比，我们的方法所需参数更少，计算成本更低，且仍能实现出色的性能。此外，我们还发现，在仅基于BERT的局部上下文分层模型中增加更多注意力机制，同时融入句子位置信息，能够提升识别效果。
英文摘要：
The legal domain is a vast and complex field that involves a considerable amount of text analysis, including laws, legal arguments, and legal opinions. Legal practitioners must analyze these texts to understand legal cases, research legal precedents, and prepare legal documents. The size of legal opinions continues to grow, making it increasingly challenging to develop a model that can accurately predict the rhetorical roles of legal opinions given their complexity and diversity. In this research paper, we propose a novel model architecture for automatically predicting rhetorical roles using pre-trained language models (PLMs) enhanced with knowledge of sentence position information within a document. Based on an annotated corpus from the LegalEval@SemEval2023 competition, we demonstrate that our approach requires fewer parameters, resulting in lower computational costs when compared to complex architectures employing a hierarchical model in a global-context, yet it achieves great performance. Moreover, we show that adding more attention to a hierarchical model based only on BERT in the local-context, along with incorporating sentence position information, enhances the results.
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。
