Resolving Legalese: A Multilingual Exploration of Negation Scope Resolution in Legal Documents

Christen, Ramona; Shaitarova, Anastassia; Stürmer, Matthias; Niklaus, Joël (25 May 2024). Resolving Legalese: A Multilingual Exploration of Negation Scope Resolution in Legal Documents In: Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). Torino, Italia. 20-25 May, 2024.

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Resolving the scope of a negation within a sentence is a challenging NLP task. The complexity of legal texts and the lack of annotated in-domain negation corpora pose challenges for state-of-the-art (SotA) models when performing negation scope resolution on multilingual legal data. Our experiments demonstrate that models pre-trained without legal data underperform in the task of negation scope resolution. Our experiments, using language models exclusively fine-tuned on domains like literary texts and medical data, yield inferior results compared to the outcomes documented in prior cross-domain experiments. We release a new set of annotated court decisions in German, French, and Italian and use it to improve negation scope resolution in both zero-shot and multilingual settings. We achieve token-level F1-scores of up to 86.7% in our zero-shot cross-lingual experiments, where the models are trained on two languages of our legal datasets and evaluated on the third. Our multilingual experiments, where the models were trained on all available negation data and evaluated on our legal datasets, resulted in F1-scores of up to 91.1%.

Item Type:	Conference or Workshop Item (Paper)
Division/Institute:	Business School > Institute for Public Sector Transformation Business School > Institute for Public Sector Transformation > Digital Sustainability Lab Business School
Name:	Christen, Ramona; Shaitarova, Anastassia; Stürmer, Matthias0000-0001-9038-4041 and Niklaus, Joël0000-0002-2779-1653
Subjects:	K Law > K Law (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Language:	German
Submitter:	Matthias Stürmer
Date Deposited:	10 Sep 2024 15:07
Last Modified:	10 Sep 2024 15:07
ARBOR DOI:	10.48550/arxiv.2309.08695
URI:	https://arbor.bfh.ch/id/eprint/22329