@inproceedings{niklaus-etal-2022-empirical,
title = "An Empirical Study on Cross-{X} Transfer for Legal Judgment Prediction",
author = {Niklaus, Joel and
St{\"u}rmer, Matthias and
Chalkidis, Ilias},
editor = "He, Yulan and
Ji, Heng and
Li, Sujian and
Liu, Yang and
Chang, Chua-Hui",
booktitle = "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)",
month = nov,
year = "2022",
address = "Online only",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.aacl-main.3",
pages = "32--46",
abstract = "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{\mbox{$\times$}} 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.",
}
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<abstract>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\times 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.</abstract>
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%0 Conference Proceedings
%T An Empirical Study on Cross-X Transfer for Legal Judgment Prediction
%A Niklaus, Joel
%A Stürmer, Matthias
%A Chalkidis, Ilias
%Y He, Yulan
%Y Ji, Heng
%Y Li, Sujian
%Y Liu, Yang
%Y Chang, Chua-Hui
%S 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)
%D 2022
%8 November
%I Association for Computational Linguistics
%C Online only
%F niklaus-etal-2022-empirical
%X 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\times 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.
%U https://aclanthology.org/2022.aacl-main.3
%P 32-46
Markdown (Informal)
[An Empirical Study on Cross-X Transfer for Legal Judgment Prediction](https://aclanthology.org/2022.aacl-main.3) (Niklaus et al., AACL-IJCNLP 2022)
ACL
- Joel Niklaus, Matthias Stürmer, and Ilias Chalkidis. 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 (Volume 1: Long Papers), pages 32–46, Online only. Association for Computational Linguistics.