JurisCTC: Enhancing Legal Judgment Prediction via Cross-Domain Transfer and Contrastive Learning

--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. T o 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. Legal Judgment Prediction (LJP) refers to the task of forecasting court outcomes based on the facts of a legal case, as well as other relevant information such as arguments and claims presented in the case description. This field aims to leverage computational techniques to predict judicial decisions, offering significant benefits across various legal contexts. Automated LJP systems have considerable practical value: they can assist legal professionals in analyzing cases and providing consultation services to the public, thereby reducing legal costs and improving access to justice.