@inproceedings{nguyen-etal-2023-vtcc,
title = "{VTCC}-{NLP} at {S}em{E}val-2023 Task 6:Long-Text Representation Based on Graph Neural Network for Rhetorical Roles Prediction",
author = "Nguyen, Hiep and
Ngo, Hoang and
Bui, Nam",
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-1.155",
doi = "10.18653/v1/2023.semeval-1.155",
pages = "1121--1126",
abstract = "Rhetorical Roles (RR) prediction is to predict the label of each sentence in legal documents, which is regarded as an emergent task for legal document understanding. In this study, we present a novel method for the RR task by exploiting the long context representation. Specifically, legal documents are known as long texts, in which previous works have no ability to consider the inherent dependencies among sentences. In this paper, we propose GNNRR (Graph Neural Network for Rhetorical Roles Prediction), which is able to model the cross-information for long texts. Furthermore, we develop multitask learning by incorporating label shift prediction (LSP) for segmenting a legal document. The proposed model is evaluated on the SemEval 2023 Task 6 - Legal Eval Understanding Legal Texts for RR sub-task. Accordingly, our method achieves the top 4 in the public leaderboard of the sub-task. Our source code is available for further investigation{\textbackslash}footnote{https://github.com/hiepnh137/SemEval2023-Task6-Rhetorical-Roles}.",
}
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<abstract>Rhetorical Roles (RR) prediction is to predict the label of each sentence in legal documents, which is regarded as an emergent task for legal document understanding. In this study, we present a novel method for the RR task by exploiting the long context representation. Specifically, legal documents are known as long texts, in which previous works have no ability to consider the inherent dependencies among sentences. In this paper, we propose GNNRR (Graph Neural Network for Rhetorical Roles Prediction), which is able to model the cross-information for long texts. Furthermore, we develop multitask learning by incorporating label shift prediction (LSP) for segmenting a legal document. The proposed model is evaluated on the SemEval 2023 Task 6 - Legal Eval Understanding Legal Texts for RR sub-task. Accordingly, our method achieves the top 4 in the public leaderboard of the sub-task. Our source code is available for further investigation\textbackslashfootnotehttps://github.com/hiepnh137/SemEval2023-Task6-Rhetorical-Roles.</abstract>
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%0 Conference Proceedings
%T VTCC-NLP at SemEval-2023 Task 6:Long-Text Representation Based on Graph Neural Network for Rhetorical Roles Prediction
%A Nguyen, Hiep
%A Ngo, Hoang
%A Bui, Nam
%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 nguyen-etal-2023-vtcc
%X Rhetorical Roles (RR) prediction is to predict the label of each sentence in legal documents, which is regarded as an emergent task for legal document understanding. In this study, we present a novel method for the RR task by exploiting the long context representation. Specifically, legal documents are known as long texts, in which previous works have no ability to consider the inherent dependencies among sentences. In this paper, we propose GNNRR (Graph Neural Network for Rhetorical Roles Prediction), which is able to model the cross-information for long texts. Furthermore, we develop multitask learning by incorporating label shift prediction (LSP) for segmenting a legal document. The proposed model is evaluated on the SemEval 2023 Task 6 - Legal Eval Understanding Legal Texts for RR sub-task. Accordingly, our method achieves the top 4 in the public leaderboard of the sub-task. Our source code is available for further investigation\textbackslashfootnotehttps://github.com/hiepnh137/SemEval2023-Task6-Rhetorical-Roles.
%R 10.18653/v1/2023.semeval-1.155
%U https://aclanthology.org/2023.semeval-1.155
%U https://doi.org/10.18653/v1/2023.semeval-1.155
%P 1121-1126
Markdown (Informal)
[VTCC-NLP at SemEval-2023 Task 6:Long-Text Representation Based on Graph Neural Network for Rhetorical Roles Prediction](https://aclanthology.org/2023.semeval-1.155) (Nguyen et al., SemEval 2023)
ACL