An Empirical Study on Cross-X Transfer for Legal Judgment Prediction
Cross-lingual transfer learning has proven useful in a variety of Natural Language Processing (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 dataset, including cases written in three languages. We find that cross-lingual transfer 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 3x 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.
Code (2)
Tasks
Cross-Lingual TransferTransfer LearningSimilar Papers 제목 키워드 기반
An Empirical Study on Cross-Lingual and Cross-Domain Transfer for Legal Judgment Prediction
Cross-lingual transfer learning has proven useful in a variety of NLP tasks, but it is understudied in the context of legal NLP, and not at all on n Legal Judgment Prediction (LJP). We explore transfer learning technique…
Cross-Lingual TransferTransfer LearningBeyond Borders: Investigating Cross-Jurisdiction Transfer in Legal Case Summarization
Legal professionals face the challenge of managing an overwhelming volume of lengthy judgments, making automated legal case summarization crucial. However, prior approaches mainly focused on training and evaluating these…
MILDSum: A Novel Benchmark Dataset for Multilingual Summarization of Indian Legal Case Judgments
Automatic summarization of legal case judgments is a practically important problem that has attracted substantial research efforts in many countries. In the context of the Indian judiciary, there is an additional complex…
Leveraging Large Language Models for Relevance Judgments in Legal Case Retrieval
Collecting relevant judgments for legal case retrieval is a challenging and time-consuming task. Accurately judging the relevance between two legal cases requires a considerable effort to read the lengthy text and a high…
Language ModelingLanguage ModellingLarge Language ModelRetrievalAR-BENCH: Benchmarking Legal Reasoning with Judgment Error Detection, Classification and Correction
Legal judgments may contain errors due to the complexity of case circumstances and the abstract nature of legal concepts, while existing appellate review mechanisms face efficiency pressures from a surge in case volumes.…
Anomaly DetectionLegal Reasoning