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

• URL: http://arxiv.org/abs/2305.04100v1
• Date: Sat, 6 May 2023 17:04:51 GMT
• Title: Rhetorical Role Labeling of Legal Documents using Transformers and Graph Neural Networks
• Authors: Anshika Gupta, Shaz Furniturewala, Vijay Kumari, Yashvardhan Sharma
• Abstract summary: 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.
• Score: 1.290382979353427
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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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