JurisCTC: Enhancing Legal Judgment Prediction via Cross-Domain Transfer and Contrastive Learning (2504.17264v1)

Abstract: In recent years, Unsupervised Domain Adaptation (UDA) has gained significant attention in the field of 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 LLMs, JurisCTC demonstrates notable advancements, achieving peak accuracies of 76.59% and 78.83%, respectively.

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

The paper, "JurisCTC: Enhancing Legal Judgment Prediction via Cross-Domain Transfer and Contrastive Learning," presents JurisCTC, an innovative model aimed at advancing the accuracy and generalization of Legal Judgment Prediction (LJP) tasks by leveraging cross-domain transfer learning and contrastive learning. This research specifically targets the challenges associated with complex legal texts and the scarcity of comprehensive annotated datasets, which are prevalent in the legal domain.

The significance of JurisCTC lies in its approach to cross-domain learning, primarily between civil law and criminal law domains. Legal Judgment Prediction is inherently challenging due to the multifaceted nature of legal texts and decisions, which often necessitate an interdisciplinary understanding of the legal processes and logical consistency applied across various cases. JurisCTC addresses these complexities by utilizing both unsupervised domain adaptation (UDA) techniques and contrastive learning to enhance the model's precision in predicting legal outcomes.

Methodology

JurisCTC includes a feature extractor based on BERT, along with domain and class classifiers that facilitate knowledge transfer across distinct legal fields. The model employs a gradient reversal strategy to encourage the learning of domain-invariant features, a method well-suited for tasks involving substantial domain discrepancies. This adversarial approach is complemented by Maximum Mean Discrepancy (MMD) and Contrastive Learning, which further align the feature spaces between different legal domains.

The incorporation of these techniques has allowed JurisCTC to achieve notable accuracy in LJP tasks. Specifically, the model attained peak accuracies of 76.59% and 78.83% when applied to criminal and civil law domains, respectively. These results not only exceed those of existing LLMs and traditional models but demonstrate JurisCTC's robust capability to adapt and generalize across varying legal contexts.

Experimental Insights

The paper details a comprehensive experimental setup that utilized both civil and criminal law datasets integrated for analysis. The results exhibited by JurisCTC underscore its efficacy in overcoming the limitations posed by domain-specific models, such as BERT and TOPJUDGE, as well as state-of-the-art LLMs like GPT-4 or Gemini-1.5-Flash. In comparative evaluations, JurisCTC consistently surpassed these baselines, thereby validating its design choices and strategic emphasis on cross-domain adaptation.

An ablation paper further revealed the importance of contrastive learning and domain adaptation strategies, highlighting how each component of JurisCTC contributes to its superior performance. The paper focused on tasks involving inter-domain transfers between civil and criminal law, showcasing the model's adaptability and the practical implications of its methodologies.

Implications and Future Directions

The enhanced performance of JurisCTC has both practical and theoretical implications. Practically, the model can be adopted to assist legal professionals in case analysis and prediction with higher accuracy, potentially reducing costs and improving access to legal services. Theoretically, the research paves the way for further exploration into domain-specific attribute learning within AI systems, particularly in contexts where inter-domain dependencies are pivotal.

Future work could include fine-tuning JurisCTC to various legal systems beyond the Chinese context, thereby further validating its adaptability and scalability. Additionally, integration with other cutting-edge models, such as multimodal AI systems, could enhance JurisCTC's capability to interpret complex legal documents comprehensively.

In conclusion, JurisCTC represents a significant advancement in the domain of legal judgment prediction, leveraging the power of cross-domain transfer and contrastive learning to exceed traditional models' efficacy. Its contributions are poised to influence the development of more sophisticated, generalizable AI systems in the legal field, marking an important step towards streamlined and accurate legal predictions.