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

arXiv: https://arxiv.org/abs/2309.08695 · HTML: https://mlsanity.com/paper/2309.08695/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 46 references that are in the map.
- First posted on arXiv 15 Sep 2023.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [LexGLUE: A Benchmark Dataset for Legal Language Understanding in English](https://mlsanity.com/paper/2110.00976/) (2021)
- [Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages](https://mlsanity.com/paper/2305.12182/) (2023)
- [MultiEURLEX -- A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer](https://mlsanity.com/paper/2109.00904/) (2021)
- [LEXTREME: A Multi-Lingual and Multi-Task Benchmark for the Legal Domain](https://mlsanity.com/paper/2301.13126/) (2023)
- [Swiss-Judgment-Prediction: A Multilingual Legal Judgment Prediction Benchmark](https://mlsanity.com/paper/2110.00806/) (2021)
- [MultiLegalPile: A 689GB Multilingual Legal Corpus](https://mlsanity.com/paper/2306.02069/) (2023)
- [NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolution](https://mlsanity.com/paper/1911.04211/) (2019)
- [An Empirical Study on Cross-X Transfer for Legal Judgment Prediction](https://mlsanity.com/paper/2209.12325/) (2022)
- [One Law, Many Languages: Benchmarking Multilingual Legal Reasoning for Judicial Support](https://mlsanity.com/paper/2306.09237/) (2023)
- [MultiLegalSBD: A Multilingual Legal Sentence Boundary Detection Dataset](https://mlsanity.com/paper/2305.01211/) (2023)
- [Reading Comprehension in Czech via Machine Translation and Cross-lingual Transfer](https://mlsanity.com/paper/2007.01667/) (2020)
- [Neural Networks for Cross-lingual Negation Scope Detection](https://mlsanity.com/paper/1810.02156/) (2018)

## Built on by (most cited first, 3 of 3)

- [LawInstruct: A Resource for Studying Language Model Adaptation to the Legal Domain](https://mlsanity.com/paper/2404.02127/) (2024)
- [Automatic Anonymization of Swiss Federal Supreme Court Rulings](https://mlsanity.com/paper/2310.04632/) (2023)
- [From No to Know: Taxonomy, Challenges, and Opportunities for Negation Understanding in Multimodal Foundation Models](https://mlsanity.com/paper/2502.09645/) (2025)

## Abstract (arXiv)

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%.