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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} |
|
} |
|
``` |
|
|
|
### Contributions |
|
|
|
[More Information Needed] |
