Resolving Legalese: A Multilingual Exploration of Negation Scope
  Resolution in Legal Documents
Ramona Christen Anastassia Shaitarova Matthias Stürmer Joel Niklaus
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%.