Rhetorical Role Labeling of Legal Documents using Transformers and Graph
  Neural Networks
Anshika Gupta Shaz Furniturewala Vijay Kumari Yashvardhan Sharma
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.