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

Anshika Gupta, Shaz Furniturewala, Vijay Kumari, Yashvardhan Sharma
5/6/2023
cs.CL

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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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Cite this paper

@article{gupta2023rhetorical,
  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},
  year   = {2023},
  eprint = {2305.04100},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2305.04100v1}
}

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