提升跨法域法律文献链接预测的鲁棒性与泛化能力
Robust Generalizable Heterogeneous Legal Link Prediction
- 引入边丢弃与特征拼接，增强表示学习鲁棒性
- 错误率最高降低45%，在新西兰数据上表现显著
- 多语言节点特征+非对称解码器，支持跨语种迁移
近期研究将链接预测应用于具有丰富元特征的大规模异构法律引文网络。我们发现，通过引入边丢弃和特征拼接，可学习更鲁棒的表示，使错误率最高降低45%。同时提出基于多语言节点特征与改进的非对称解码器的方法，提升模型在地理与语言差异较大的新西兰数据上的兼容性，实现跨法域预测扩展。该方法还增强了不同法律体系间的归纳迁移能力。
原文摘要 · Abstract (English)
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.
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