JurisCTC：通过跨领域迁移与对比学习增强法律判决预测
JurisCTC: Enhancing Legal Judgment Prediction via Cross-Domain Transfer and Contrastive Learning
AI总结：
本文提出JurisCTC模型，通过跨民事与刑事法律领域的知识迁移和对比学习，在有限标注数据下提升法律判决预测准确性，最高准确率达78.83%。
摘要：
近年来，无监督领域自适应（UDA）因其能够增强模型在不同领域间的泛化能力，在自然语言处理（NLP）领域获得了广泛关注。然而，其在不同法律领域之间进行知识迁移的应用仍基本未被探索。为应对冗长复杂的法律文本以及大规模标注数据集有限所带来的挑战，我们提出了JurisCTC，这是一种旨在提升法律判决预测（LJP）任务准确性的新型模型。与现有方法不同，JurisCTC能够促进跨不同法律领域的有效知识迁移，并采用对比学习来区分来自不同领域的样本。具体而言，在LJP任务中，我们实现了民事与刑事法律领域之间的知识迁移。与其他模型以及特定的大语言模型（LLMs）相比，JurisCTC展现出显著进步，分别达到了76.59%和78.83%的最高准确率。
英文摘要：
In recent years, Unsupervised Domain Adaptation (UDA) has gained significant attention in the field of Natural Language Processing (NLP) owing to its ability to enhance model generalization across diverse domains. However, its application for knowledge transfer between distinct legal domains remains largely unexplored. To address the challenges posed by lengthy and complex legal texts and the limited availability of large-scale annotated datasets, we propose JurisCTC, a novel model designed to improve the accuracy of Legal Judgment Prediction (LJP) tasks. Unlike existing approaches, JurisCTC facilitates effective knowledge transfer across various legal domains and employs contrastive learning to distinguish samples from different domains. Specifically, for the LJP task, we enable knowledge transfer between civil and criminal law domains. Compared to other models and specific large language models (LLMs), JurisCTC demonstrates notable advancements, achieving peak accuracies of 76.59% and 78.83%, respectively.
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。
