@inproceedings{gupta-etal-2023-steno,
title = "Steno {AI} at {S}em{E}val-2023 Task 6: Rhetorical Role Labelling of Legal Documents using Transformers and Graph Neural Networks",
author = "Gupta, Anshika and
Furniturewala, Shaz and
Kumari, Vijay and
Sharma, Yashvardhan",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.256",
doi = "10.18653/v1/2023.semeval-1.256",
pages = "1858--1862",
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. 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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<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. 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.</abstract>
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%0 Conference Proceedings
%T Steno AI at SemEval-2023 Task 6: Rhetorical Role Labelling of Legal Documents using Transformers and Graph Neural Networks
%A Gupta, Anshika
%A Furniturewala, Shaz
%A Kumari, Vijay
%A Sharma, Yashvardhan
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F gupta-etal-2023-steno
%X 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. 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.
%R 10.18653/v1/2023.semeval-1.256
%U https://aclanthology.org/2023.semeval-1.256
%U https://doi.org/10.18653/v1/2023.semeval-1.256
%P 1858-1862
Markdown (Informal)
[Steno AI at SemEval-2023 Task 6: Rhetorical Role Labelling of Legal Documents using Transformers and Graph Neural Networks](https://aclanthology.org/2023.semeval-1.256) (Gupta et al., SemEval 2023)
ACL