Legal Judgment Prediction (LJP) is a judicial assistance system that recommends the legal components such as applicable statues, prison term and penalty term by analyzing the given input case document. Indian legal system is in the need of technical assistance such as artificial intelligence to solve the crores of pending cases in various courts for years and its being increased day to day. Most of the existing Indian models did not adequately concentrate on the semantics embedded in the fact description (FD) that impacts the decision. The proposed semantic extraction based LJP (SLJP) model provides the advantages of pretrained transformers for complex unstructured legal case document understanding and to generate embeddings. The model draws the in-depth semantics of the given FD at multiple levels i.e., chunk and case document level by following the divide and conquer approach. It creates the concise view of the given fact description using the extracted semantics as per the original court case document structure and predicts judgment using attention mechanism. We tested the model performance on two available Indian datasets Indian Legal Documents corpus (ILDC) and Indian Legal Statue Identification (ILSI) and got promising results. Also shown the highest performance and less performance degradation for increased epochs than base models on ILDC dataset.

翻译：法律判决预测（LJP）是一种司法辅助系统，通过分析输入的案件文档，推荐适用法条、刑期和罚金等法律要素。印度司法系统亟需人工智能等技术手段，以解决各级法院积压数十年且日益增长的数千万件待审案件。现有印度模型大多未能充分关注案件事实描述（FD）中影响判决的语义信息。本文提出的基于语义提取的法律判决预测（SLJP）模型，利用预训练Transformer处理复杂非结构化法律文档并生成嵌入表示。该模型采用分治策略，在段落级和案件文档级多层面提取给定FD的深层语义，并依据原始法院案件文档结构，利用提取的语义生成事实描述的简洁视图，通过注意力机制进行判决预测。我们在两个印度数据集——印度法律文档语料库（ILDC）和印度法律条文识别（ILSI）上测试了模型性能，取得了良好结果。在ILDC数据集上，该模型相比基线模型展现出更高的性能，且随着训练轮次增加，性能退化更少。