In this paper, we cast Legal Judgment Prediction (LJP) from text on European Court of Human Rights cases as an entailment task, where the case outcome is classified from a combined input of case facts and convention articles. This configuration facilitates the model learning legal reasoning ability in mapping article text to specific fact text. It also provides the opportunity to evaluate the model's ability to generalize to zero-shot settings when asked to classify the case outcome with respect to articles not seen during training. We devise zero-shot LJP experiments and apply domain adaptation methods based on domain discriminator and Wasserstein distance. Our results demonstrate that the entailment architecture outperforms straightforward fact classification. We also find that domain adaptation methods improve zero-shot transfer performance, with article relatedness and encoder pre-training influencing the effect.

翻译：本文将欧洲人权法院案件文本中的法律判决预测（LJP）重构为蕴含任务，通过联合输入案件事实与公约条款对案件结果进行分类。该配置有助于模型学习将条款文本映射至特定事实文本的法律推理能力，同时提供了评估模型在训练时未见条款条件下零样本泛化能力的机会。我们设计了零样本LJP实验，并应用基于域判别器与Wasserstein距离的域自适应方法。实验结果表明，蕴含架构优于直接的事实分类方法。此外，域自适应方法可提升零样本迁移性能，且条款关联度与编码器预训练对效果具有调节作用。