在针对欧洲人权法院案件的法律判决预测中引入先例
Incorporating Precedents for Legal Judgement Prediction on European Court of Human Rights Cases
AI总结：
受遵循先例原则启发，本文提出在训练阶段通过先例融合模块整合先例并联合训练检索器与LJP模型，显著提升了欧洲人权法院案件的判决预测性能，尤其对稀疏条款效果显著。
摘要：
受遵循先例法律原则的启发，该原则利用先例（先前案例）进行知情决策，我们探索了将先例整合到LJP模型中的方法。为促进先例检索，我们基于案件间被指控条款的重叠率，以细粒度相关性信号训练了一个检索器。我们研究了两种整合先例的策略：在推理时通过基于案件邻近度的标签插值进行直接整合，以及在训练时通过使用堆叠交叉注意力模型的先例融合模块进行整合。我们采用检索器和LJP模型的联合训练，以解决它们之间的潜在空间差异。我们在ECHR管辖区的LJP任务上的实验表明，在训练期间整合先例并结合检索器和LJP模型的联合训练，其表现优于没有先例或仅在推理时整合先例的模型，尤其对稀疏条款更有利。
英文摘要：
Inspired by the legal doctrine of stare decisis, which leverages precedents (prior cases) for informed decision-making, we explore methods to integrate them into LJP models. To facilitate precedent retrieval, we train a retriever with a fine-grained relevance signal based on the overlap ratio of alleged articles between cases. We investigate two strategies to integrate precedents: direct incorporation at inference via label interpolation based on case proximity and during training via a precedent fusion module using a stacked-cross attention model. We employ joint training of the retriever and LJP models to address latent space divergence between them. Our experiments on LJP tasks from the ECHR jurisdiction reveal that integrating precedents during training coupled with joint training of the retriever and LJP model, outperforms models without precedents or with precedents incorporated only at inference, particularly benefiting sparser articles.
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。
