Recent work has applied link prediction to large heterogeneous legal citation networks \new{with rich meta-features}. We find that this approach can be improved by including edge dropout and feature concatenation for the learning of more robust representations, which reduces error rates by up to 45%. We also propose an approach based on multilingual node features with an improved asymmetric decoder for compatibility, which allows us to generalize and extend the prediction to more, geographically and linguistically disjoint, data from New Zealand. Our adaptations also improve inductive transferability between these disjoint legal systems.

翻译：近期研究已将链接预测应用于具有丰富元特征的大型异构法律引文网络。我们发现，通过引入边丢弃和特征拼接来学习更鲁棒的表征，可将错误率降低高达45%。此外，我们提出一种基于多语言节点特征的改进方法，采用增强的非对称解码器以提升兼容性，从而能够将预测泛化并扩展至新西兰在地理和语言上更为离散的数据。我们的改进方案还提升了这些离散法律系统间的归纳迁移能力。