通过跨领域迁移与对比学习，提升法律判决预测准确率。
JurisCTC: Enhancing Legal Judgment Prediction via Cross-Domain Transfer and Contrastive Learning
- 利用跨域迁移与对比学习，实现民事与刑事法律知识互推
- 在两类法律任务中分别达到76.59%和78.83%准确率
- 适合法律AI研究者及司法智能化系统开发者
近年来，无监督域适应（UDA）因其提升模型跨领域泛化能力的潜力，在自然语言处理领域受到广泛关注。然而，其在不同法律领域间的知识迁移应用仍处于空白。针对法律文本长且复杂、大规模标注数据稀缺的问题，本文提出JurisCTC模型，旨在提升法律判决预测（LJP）的准确性。与现有方法不同，JurisCTC支持民事与刑事法律领域的有效知识迁移，并采用对比学习区分不同领域样本。实验表明，相比其他模型及特定大语言模型，JurisCTC在两类任务中分别取得76.59%和78.83%的最高准确率。
原文摘要 · Abstract (English)
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.
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