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

Niklaus, Joël; Chalkidis, Ilias; Stürmer, Matthias (2 October 2021). Swiss-Judgment-Prediction: A Multilingual Legal Judgment Prediction Benchmark In: Aletras, Nikolaos; Androutsopoulos, Ion; Barrett, Leslie; Goanta, Catalina; Preotiuc-Pietro, Daniel (eds.) Proceedings of the Natural Legal Language Processing Workshop 2021. Stroudsburg PA, USA: Association for Computational Linguistics

[img]
Preview
Text
2110.00806.pdf
Available under License Creative Commons: Attribution (CC-BY).

Download (1MB) | Preview

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 Switzerland (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.

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;
Chalkidis, Ilias;
Stürmer, Matthias0000-0001-9038-4041;
Aletras, Nikolaos;
Androutsopoulos, Ion;
Barrett, Leslie;
Goanta, Catalina and
Preotiuc-Pietro, Daniel

Publisher:

Association for Computational Linguistics

Language:

English

Submitter:

Joël Niklaus

Date Deposited:

07 Aug 2023 16:07

Last Modified:

07 Aug 2023 16:22

Related URLs:

ARBOR DOI:

10.24451/arbor.19665

URI:

https://arbor.bfh.ch/id/eprint/19665

Actions (login required)

View Item View Item
Provide Feedback