An empirical study on cross-x transfer for legal judgment prediction

Niklaus, Joël; Stürmer, Matthias; Chalkidis, Ilias (23 November 2022). An empirical study on cross-x transfer for legal judgment prediction In: Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (pp. 32-46). Stroudsburg PA, USA: Association for Computational Linguistics

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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.

Item Type:	Conference or Workshop Item (Paper)
Division/Institute:	Business School > Institute for Public Sector Transformation Business School > Institute for Public Sector Transformation > Data and Infrastructure Business School
Name:	Niklaus, Joël0000-0002-2779-1653; Stürmer, Matthias0000-0001-9038-4041 and Chalkidis, Ilias
Publisher:	Association for Computational Linguistics
Language:	English
Submitter:	Joël Niklaus
Date Deposited:	07 Aug 2023 16:35
Last Modified:	07 Aug 2023 16:35
Related URLs:	Organisation Publication
ARBOR DOI:	10.24451/arbor.19687
URI:	https://arbor.bfh.ch/id/eprint/19687