no code implementations • 26 Feb 2024 • Santosh T. Y. S. S, Nina Baumgartner, Matthias Stürmer, Matthias Grabmair, Joel Niklaus
The assessment of explainability in Legal Judgement Prediction (LJP) systems is of paramount importance in building trustworthy and transparent systems, particularly considering the reliance of these systems on factors that may lack legal relevance or involve sensitive attributes.
no code implementations • 7 Oct 2023 • Joel Niklaus, Robin Mamié, Matthias Stürmer, Daniel Brunner, Marcel Gygli
Releasing court decisions to the public relies on proper anonymization to protect all involved parties, where necessary.
1 code implementation • 15 Sep 2023 • Ramona Christen, Anastassia Shaitarova, Matthias Stürmer, Joel Niklaus
Resolving the scope of a negation within a sentence is a challenging NLP task.
1 code implementation • 22 Aug 2023 • Alex Nyffenegger, Matthias Stürmer, Joel Niklaus
Anonymity of both natural and legal persons in court rulings is a critical aspect of privacy protection in the European Union and Switzerland.
2 code implementations • 15 Jun 2023 • Vishvaksenan Rasiah, Ronja Stern, Veton Matoshi, Matthias Stürmer, Ilias Chalkidis, Daniel E. Ho, Joel Niklaus
In this paper, we introduce a novel NLP benchmark that poses challenges to current LLMs across four key dimensions: processing long documents (up to 50K tokens), utilizing domain specific knowledge (embodied in legal texts), multilingual understanding (covering five languages), and multitasking (comprising legal document to document Information Retrieval, Court View Generation, Leading Decision Summarization, Citation Extraction, and eight challenging Text Classification tasks).
no code implementations • 3 Jun 2023 • Joel Niklaus, Veton Matoshi, Matthias Stürmer, Ilias Chalkidis, Daniel E. Ho
Large, high-quality datasets are crucial for training Large Language Models (LLMs).
1 code implementation • 2 May 2023 • Tobias Brugger, Matthias Stürmer, Joel Niklaus
Sentence Boundary Detection (SBD) is one of the foundational building blocks of Natural Language Processing (NLP), with incorrectly split sentences heavily influencing the output quality of downstream tasks.
1 code implementation • 30 Jan 2023 • Joel Niklaus, Veton Matoshi, Pooja Rani, Andrea Galassi, Matthias Stürmer, Ilias Chalkidis
To provide a fair comparison, we propose two aggregate scores, one based on the datasets and one on the languages.
2 code implementations • 25 Sep 2022 • Joel Niklaus, Matthias Stürmer, Ilias Chalkidis
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.
1 code implementation • EMNLP (NLLP) 2021 • Joel Niklaus, Ilias Chalkidis, Matthias Stürmer
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).