@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}.",
}

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}.
Nguyen, Hiep Ngo, Hoang Bui, Nam
VTCC-NLP at SemEval-2023 Task 6:Long-Text Representation Based on Graph Neural Network for Rhetorical Roles Prediction