A legal document is usually long and dense requiring human effort to parse it. It also contains significant amounts of jargon which make deriving insights from it using existing models a poor approach. This paper presents the approaches undertaken to perform the task of rhetorical role labelling on Indian Court Judgements as part of SemEval Task 6: understanding legal texts, shared subtask A. We experiment with graph based approaches like Graph Convolutional Networks and Label Propagation Algorithm, and transformer-based approaches including variants of BERT to improve accuracy scores on text classification of complex legal documents.
@article{arxiv.2305.04100,
title = {Rhetorical Role Labeling of Legal Documents using Transformers and Graph Neural Networks},
author = {Anshika Gupta and Shaz Furniturewala and Vijay Kumari and Yashvardhan Sharma},
journal= {arXiv preprint arXiv:2305.04100},
year = {2023}
}