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：在本文中,我们从欧洲人权法院案件案文中将法律判决预测(LJP)作为一项必然任务,将案件结果从案件事实和公约条款的综合投入中分类,这种配置有利于示范学习法律推理能力,将条款文本映射成具体事实文本,还提供机会评价模型在被要求对培训期间未见的物品进行案件结果分类时,是否有能力将案件结果归纳为零。我们设计了零弹LJP实验,并应用了基于领域歧视者和瓦塞斯坦距离的域适应方法。我们的结果显示,所涉结构超越了直接的事实分类。我们还发现,域适应方法提高了零射转移的性能,与影响其效果的相关条款和编码器前培训相结合。