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Rhetorical Role Labeling of Legal Documents using Transformers and Graph Neural Networks by Gupta, Anshika; Furniturewala, Shaz; Kumari, Vijay; Sharma, Yashvardhan is a scholarly article available to read on EtoBox.

What is Rhetorical Role Labeling of Legal Documents using Transformers and Graph Neural Networks about?

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.

Author

Gupta, Anshika; Furniturewala, Shaz; Kumari, Vijay; Sharma, Yashvardhan

Published

2023

Language

EN