Predicting the judgment of a legal case from its unannotated case facts is a challenging task. The lengthy and non-uniform document structure poses an even greater challenge in extracting information for decision prediction. In this work, we explore and propose a two-level classification mechanism; both supervised and unsupervised; by using domain-specific pre-trained BERT to extract information from long documents in terms of sentence embeddings further processing with transformer encoder layer and use unsupervised clustering to extract hidden labels from these embeddings to better predict a judgment of a legal case. We conduct several experiments with this mechanism and see higher performance gains than the previously proposed methods on the ILDC dataset. Our experimental results also show the importance of domain-specific pre-training of Transformer Encoders in legal information processing.

翻译：从未标注的案件事实预测法律判决是一项具有挑战性的任务。冗长且结构不统一的文档使得信息提取以支持判决预测更加困难。本文探索并提出了一种双层分类机制，包括监督和无监督方法：利用领域特定的预训练BERT从长文档中提取信息，以句子嵌入的形式进行处理，再通过Transformer编码器层进一步处理，并采用无监督聚类从这些嵌入中提取隐藏标签，以更准确地预测法律案件的判决。我们通过该机制进行了多项实验，在ILDC数据集上取得了比先前方法更高的性能提升。实验结果还表明了领域特定预训练Transformer编码器在法律信息处理中的重要性。