Logical Structure-based Pretrained Models for Legal Text Processing

Ha Thanh Nguyen, Le Minh Nguyen

2022

Abstract

In recent years, we have witnessed breakthroughs in natural language processing coming from pretrained models based on the Transformer architecture. In the field of legal text processing, a special sub-domain of NLP, pretrained models also show promising results. For a legal sentence, although the natural language is used for expression, the real meaning lies in its logical structure. From that observation, we have a hypothesis that the knowledge of recognizing logical structures can support deep learning models to understand the legal text better and achieve a higher performance in the related tasks. To verify our assumption, we design a novel framework to inject the knowledge about recognizing the requisite and effectuation part of a law sentence into Transformer models. Our proposed method is effective and general. By our experiments, we provide informative results about our approach and its performance compared with the baselines.

Paper Citation

in Harvard Style

Nguyen H. and Nguyen L. (2022). Logical Structure-based Pretrained Models for Legal Text Processing. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 524-531. DOI: 10.5220/0010852000003116

in Bibtex Style

@conference{icaart22,
author={Ha Thanh Nguyen and Le Minh Nguyen},
title={Logical Structure-based Pretrained Models for Legal Text Processing},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2022},
pages={524-531},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010852000003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Logical Structure-based Pretrained Models for Legal Text Processing
SN - 978-989-758-547-0
AU - Nguyen H.
AU - Nguyen L.
PY - 2022
SP - 524
EP - 531
DO - 10.5220/0010852000003116