Given the fact description text of a legal case, legal judgment prediction (LJP) aims to predict the case's charge, law article and penalty term. A core problem of LJP is how to distinguish confusing legal cases, where only subtle text differences exist. Previous studies fail to distinguish different classification errors with a standard cross-entropy classification loss, and ignore the numbers in the fact description for predicting the term of penalty. To tackle these issues, in this work, first, we propose a moco-based supervised contrastive learning to learn distinguishable representations, and explore the best strategy to construct positive example pairs to benefit all three subtasks of LJP simultaneously. Second, in order to exploit the numbers in legal cases for predicting the penalty terms of certain cases, we further enhance the representation of the fact description with extracted crime amounts which are encoded by a pre-trained numeracy model. Extensive experiments on public benchmarks show that the proposed method achieves new state-of-the-art results, especially on confusing legal cases. Ablation studies also demonstrate the effectiveness of each component.
翻译:鉴于法律案件的事实描述文本,法律判决预测(LJP)旨在预测案件的指控、法律条款和惩罚术语。LJP的一个核心问题是如何区分混乱的法律案件,因为只有微妙的文本差异存在。以前的研究未能区分不同的分类错误和标准的跨热带分类损失,忽略了用于预测惩罚期限的事实描述中的数字。为了解决这些问题,我们首先提议在这项工作中进行基于模型的监督性对比学习,以了解不同的表述,并探索建立积极的范例对等的最佳战略,同时使LJP的所有三个子任务都受益。第二,为了利用法律案件中的数字来预测某些案件的处罚条件,我们进一步加强对通过事先训练的算术模型编码的已提取的犯罪数量的事实描述。关于公共基准的广泛实验表明,拟议的方法取得了新的最新结果,特别是在令人困惑的法律案例方面。计算研究还表明每个组成部分的有效性。