# MultiLegalSBD: A Multilingual Legal Sentence Boundary Detection Dataset

arXiv: https://arxiv.org/abs/2305.01211 · HTML: https://mlsanity.com/paper/2305.01211/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 37 references that are in the map.
- First posted on arXiv 2 May 2023.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [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)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [MultiEURLEX -- A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer](https://mlsanity.com/paper/2109.00904/) (2021)
- [Fast WordPiece Tokenization](https://mlsanity.com/paper/2012.15524/) (2020)
- [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)
- [EUR-Lex-Sum: A Multi- and Cross-lingual Dataset for Long-form Summarization in the Legal Domain](https://mlsanity.com/paper/2210.13448/) (2022)
- [An Empirical Study on Cross-X Transfer for Legal Judgment Prediction](https://mlsanity.com/paper/2209.12325/) (2022)
- [BudgetLongformer: Can we Cheaply Pretrain a SotA Legal Language Model From Scratch?](https://mlsanity.com/paper/2211.17135/) (2022)
- [Lex Rosetta: Transfer of Predictive Models Across Languages, Jurisdictions, and Legal Domains](https://mlsanity.com/paper/2112.07882/) (2021)
- [LegalRelectra: Mixed-domain Language Modeling for Long-range Legal Text Comprehension](https://mlsanity.com/paper/2212.08204/) (2022)

## Built on by (most cited first, 7 of 7)

- [Segment Any Text: A Universal Approach for Robust, Efficient and Adaptable Sentence Segmentation](https://mlsanity.com/paper/2406.16678/) (2024)
- [Resolving Legalese: A Multilingual Exploration of Negation Scope Resolution in Legal Documents](https://mlsanity.com/paper/2309.08695/) (2023)
- [Automatic Anonymization of Swiss Federal Supreme Court Rulings](https://mlsanity.com/paper/2310.04632/) (2023)
- [Precise Legal Sentence Boundary Detection for Retrieval at Scale: NUPunkt and CharBoundary](https://mlsanity.com/paper/2504.04131/) (2025)
- [Evaluation of Large Language Models in Legal Applications: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/2601.15267/) (2026)
- [Strategies for political-statement segmentation and labelling in unstructured text](https://mlsanity.com/paper/2503.07179/) (2025)
- [Classifying Interpretive Canons at the Sentence Level: A Benchmark from the German Federal Constitutional Court](https://mlsanity.com/paper/2609.26945/) (2026)

## Abstract (arXiv)

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. It is a challenging task for algorithms, especially in the legal domain, considering the complex and different sentence structures used. In this work, we curated a diverse multilingual legal dataset consisting of over 130'000 annotated sentences in 6 languages. Our experimental results indicate that the performance of existing SBD models is subpar on multilingual legal data. We trained and tested monolingual and multilingual models based on CRF, BiLSTM-CRF, and transformers, demonstrating state-of-the-art performance. We also show that our multilingual models outperform all baselines in the zero-shot setting on a Portuguese test set. To encourage further research and development by the community, we have made our dataset, models, and code publicly available.