Swiss-Judgment-Prediction: A Multilingual Legal Judgment Prediction Benchmark

Joel Niklaus, Ilias Chalkidis, Matthias Stürmer


Abstract
In many jurisdictions, the excessive workload of courts leads to high delays. Suitable predictive AI models can assist legal professionals in their work, and thus enhance and speed up the process. So far, Legal Judgment Prediction (LJP) datasets have been released in English, French, and Chinese. We publicly release a multilingual (German, French, and Italian), diachronic (2000-2020) corpus of 85K cases from the Federal Supreme Court of Switzer- land (FSCS). We evaluate state-of-the-art BERT-based methods including two variants of BERT that overcome the BERT input (text) length limitation (up to 512 tokens). Hierarchical BERT has the best performance (approx. 68-70% Macro-F1-Score in German and French). Furthermore, we study how several factors (canton of origin, year of publication, text length, legal area) affect performance. We release both the benchmark dataset and our code to accelerate future research and ensure reproducibility.
Anthology ID:
2021.nllp-1.3
Volume:
Proceedings of the Natural Legal Language Processing Workshop 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venues:
EMNLP | NLLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
19–35
Language:
URL:
https://aclanthology.org/2021.nllp-1.3
DOI:
10.18653/v1/2021.nllp-1.3
Bibkey:
Cite (ACL):
Joel Niklaus, Ilias Chalkidis, and Matthias Stürmer. 2021. Swiss-Judgment-Prediction: A Multilingual Legal Judgment Prediction Benchmark. In Proceedings of the Natural Legal Language Processing Workshop 2021, pages 19–35, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Swiss-Judgment-Prediction: A Multilingual Legal Judgment Prediction Benchmark (Niklaus et al., NLLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.nllp-1.3.pdf
Code
 joelniklaus/swissjudgementprediction
Data
ECHRECtHR