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task_categories:
- token-classification
language:
- en
- es
- de
- it
- pt
- fr
pretty_name: 'MultiLegalSBD: A Multilingual Legal Sentence Boundary Detection Dataset'
size_categories:
- 100K<n<1M
Dataset Card for Dataset Name
Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
Dataset Summary
This is a multilingual dataset containing ~130k annotated sentence boundaries. It contains laws and court decision in 6 different languages.
Supported Tasks and Leaderboards
[More Information Needed]
Languages
English, French, Italian, German, Portuguese, Spanish
Dataset Structure
It is structured in the following format: {language}_{type}_{shard}.jsonl.xz
type is one of the following:
- laws
- judgements
Use the the dataset like this:
from datasets import load_dataset
config = 'fr_laws' #{language}_{type} | to load all languages and/or all types, use 'all_all'
dataset = load_dataset('rdcs/MultiLegalSBD', config)
Data Instances
[More Information Needed]
Data Fields
- text: the original text
- spans:
- start: offset of the first character
- end: offset of the last character
- label: One label only -> Sentence
- token_start: id of the first token
- token_end: id of the last token
- tokens:
- text: token text
- start: offset of the first character
- end: offset of the last character
- id: token id
- ws: whether the token is followed by whitespace
Data Splits
There is only one split available
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
[More Information Needed]
Annotations
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
[More Information Needed]
Licensing Information
[More Information Needed]
Citation Information
@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}
}
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