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.

使用领域特定的预训练 BERT 模型提取长文档的句子嵌入，并通过 Transformer 编码层进行进一步处理，并利用无监督聚类从这些嵌入中提取隐藏标签，以更好地预测法律案例的判断结果。在 ILDC 数据集上的多次实验表明，该机制的性能提高超过先前提出的方法。实验结果也显示了法律信息处理中领域特定预训练 Transformer 编码器的重要性。

探索半监督层次化堆叠编码器在法律判决预测中的应用

Exploring Semi-supervised Hierarchical Stacked Encoder for Legal  Judgement Prediction

Every legal case sets a precedent by developing the law in one of the
following two ways. It either expands its scope, in which case it sets positive
precedent, or it narrows it, in which case it sets negative precedent. Legal
outcome prediction, the prediction of positive outcome, is an increasingly
popular task in AI. In contrast, we turn our focus to negative outcomes here,
and introduce a new task of negative outcome prediction. We discover an
asymmetry in existing models' ability to predict positive and negative
outcomes. Where the state-of-the-art outcome prediction model we used predicts
positive outcomes at 75.06 F1, it predicts negative outcomes at only 10.09 F1,
worse than a random baseline. To address this performance gap, we develop two
new models inspired by the dynamics of a court process. Our first model
significantly improves positive outcome prediction score to 77.15 F1 and our
second model more than doubles the negative outcome prediction performance to
24.01 F1. Despite this improvement, shifting focus to negative outcomes reveals
that there is still much room for improvement for outcome prediction models.

本文介绍了一项新任务 —— 负面结果预测，并提出了两种基于法庭流程动态的模型来解决现有模型在此任务上的性能差距，使得负面结果预测表现得以提高。