Rhetorical Role Labeling of Legal Documents using Transformers and Graph Neural Networks

Anshika Gupta; Shaz Furniturewala; Vijay Kumari; Yashvardhan Sharma

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arXiv · 2305.04100

Rhetorical Role Labeling of Legal Documents using Transformers and Graph Neural Networks

Anshika Gupta·Shaz Furniturewala·Vijay Kumari·Yashvardhan Sharma

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Abstract

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.

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Anshika Gupta, Shaz Furniturewala, Vijay Kumari, Yashvardhan Sharma. 2023-05-06. Rhetorical Role Labeling of Legal Documents using Transformers and Graph Neural Networks. https://arxiv.org/abs/2305.04100

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