An Empirical Study on Cross-X Transfer for Legal Judgment Prediction
An Empirical Study on Cross-X Transfer for Legal Judgment Prediction
复制标题
法律判决预测的跨X迁移实证研究
DOI：
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Ilias Chalkidis
中科院分区：
文献类型：
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作者：
Joel Niklaus;Matthias Sturmer;Ilias Chalkidis
跨语言迁移学习已被证明在各种自然语言（NLP）任务中很有用，但它在法律的NLP的背景下研究不足，而在法律的判断预测（LJP）中根本没有研究。我们使用三种语言的Swiss-Judgment-Prediction（SJP）数据集探索LJP上的迁移学习技术，包括用三种语言编写的案例。我们发现跨语言迁移（CLT）改善了跨语言的整体结果，特别是当我们使用基于适配器的微调时。最后，我们通过使用3倍大的训练语料库，用原始文档的机器翻译版本来增强训练数据集，从而进一步提高模型的性能。在此基础上，我们分析了跨领域和跨地区转移的影响，即，跨域（法律的领域）或区域训练模型。我们发现，在这两种情况下（法律的领域，原产地），在所有群体中训练的模型总体上表现更好，同时在最坏情况下也有更好的结果。最后，我们报告了当我们雄心勃勃地应用跨司法管辖区转移时的改进结果，在那里我们进一步用印度的法律的案例来增加我们的数据集。
Cross-lingual transfer learning has proven useful in a variety of Natural Language (NLP) tasks, but it is understudied in the context of legal NLP, and not at all in Legal Judgment Prediction (LJP). We explore transfer learning techniques on LJP using the trilingual Swiss-Judgment-Prediction (SJP) dataset, including cases written in three languages. We find that Cross-Lingual Transfer (CLT) improves the overall results across languages, especially when we use adapter-based fine-tuning. Finally, we further improve the model’s performance by augmenting the training dataset with machine-translated versions of the original documents, using a 3× larger training corpus. Further on, we perform an analysis exploring the effect of cross-domain and cross-regional transfer, i.e., train a model across domains (legal areas), or regions. We find that in both settings (legal areas, origin regions), models trained across all groups perform overall better, while they also have improved results in the worst-case scenarios. Finally, we report improved results when we ambitiously apply cross-jurisdiction transfer, where we further augment our dataset with Indian legal cases.
影响因子：
3.8
作者：
Aletras, Nikolaos;Tsarapatsanis, Dimitrios;Lampos, Vasileios
通讯作者：
Lampos, Vasileios