We report on an experiment in case outcome classification on European Court of Human Rights cases where our model first learns to identify the convention articles allegedly violated by the state from case facts descriptions, and subsequently uses that information to classify whether the court finds a violation of those articles. We assess the dependency between these two tasks at the feature and outcome level. Furthermore, we leverage a hierarchical contrastive loss to pull together article-specific representations of cases at the higher level, leading to distinctive article clusters. The cases in each article cluster are further pulled closer based on their outcome, leading to sub-clusters of cases with similar outcomes. Our experiment results demonstrate that, given a static pre-trained encoder, our models produce a small but consistent improvement in classification performance over single-task and joint models without contrastive loss.

翻译：我们报告了一项针对欧洲人权法院案件结果分类的实验：在该实验中，模型首先学习从案件事实描述中识别出国家据称违反的《公约》条款，随后利用该信息对法院是否认定违反这些条款进行分类。我们在特征与结果层面评估了这两项任务之间的依赖性。此外，我们采用层次化对比损失，在更高层次上将具有特定条款表征的案件聚合，从而形成清晰的条款聚类。每个条款聚类中的案件进一步基于其结果被拉近，形成具有相似结果的子聚类。实验结果表明，在采用静态预训练编码器的条件下，与不含对比损失的单任务及联合模型相比，我们的模型在分类性能上取得了虽小但一致的提升。