An Empirical Study on Cross-X Transfer for Legal Judgment Prediction
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Standard
An Empirical Study on Cross-X Transfer for Legal Judgment Prediction. / Niklaus, Joel ; Stürmer, Matthias; Chalkidis, Ilias.
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 (Volume 1: Long Papers). Association for Computational Linguistics, 2022. p. 32–46.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - GEN
T1 - An Empirical Study on Cross-X Transfer for Legal Judgment Prediction
AU - Niklaus, Joel
AU - Stürmer, Matthias
AU - Chalkidis, Ilias
PY - 2022
Y1 - 2022
N2 - 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
AB - 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
M3 - Article in proceedings
SP - 32
EP - 46
BT - 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 (Volume 1: Long Papers)
PB - Association for Computational Linguistics
T2 - 12th International Joint Conference on Natural Language Processing
Y2 - 20 November 2022 through 23 November 2022
ER -
ID: 339157386