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.
@article{arxiv.2504.17264,
title = {JurisCTC: Enhancing Legal Judgment Prediction via Cross-Domain Transfer and Contrastive Learning},
author = {Zhaolu Kang and Hongtian Cai and Xiangyang Ji and Jinzhe Li and Nanfei Gu},
journal= {arXiv preprint arXiv:2504.17264},
year = {2025}
}
Comments
Accepted in International Joint Conference on Neural Networks (IJCNN) 2025