| ## Citation | |
| ``` | |
| @inproceedings{10.1145/3594536.3595132, | |
| author = {Brugger, Tobias and St\"{u}rmer, Matthias and Niklaus, Joel}, | |
| title = {MultiLegalSBD: A Multilingual Legal Sentence Boundary Detection Dataset}, | |
| year = {2023}, | |
| isbn = {9798400701979}, | |
| publisher = {Association for Computing Machinery}, | |
| address = {New York, NY, USA}, | |
| url = {https://doi.org/10.1145/3594536.3595132}, | |
| doi = {10.1145/3594536.3595132}, | |
| abstract = {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.}, | |
| booktitle = {Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law}, | |
| pages = {42–51}, | |
| numpages = {10}, | |
| keywords = {Natural Language Processing, Sentence Boundary Detection, Text Annotation, Legal Document Analysis, Multilingual}, | |
| location = {Braga, Portugal}, | |
| series = {ICAIL '23} | |
| } | |
| ``` |