Rhetorical Role Labeling of Legal Documents using Transformers and Graph Neural Networks
This work addresses the challenge of automating legal document analysis for legal professionals, but it is incremental as it builds on existing methods for a specific shared task.
The paper tackled the problem of rhetorical role labeling in long, jargon-heavy Indian court judgments by experimenting with graph-based and transformer-based methods, achieving improved accuracy scores on the SemEval Task 6 subtask A.
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.