Court Judgement Prediction with Explanation (CJPE) is a task in the field of legal analysis and evaluation, which involves predicting the outcome of a court case based on the available legal text and providing a detailed explanation of the prediction. This is an important task in the legal system as it can aid in decision-making and improve the efficiency of the court process. In this paper, we present a new approach to understanding legal texts, which are normally long documents, based on data-oriented methods. Specifically, we first try to exploit the characteristic of data to understand the legal texts. The output is then used to train the model using the Longformer architecture. Regarding the experiment, the proposed method is evaluated on the sub-task CJPE of the SemEval-2023 Task 6. Accordingly, our method achieves top 1 and top 2 on the classification task and explanation task, respectively. Furthermore, we present several open research issues for further investigations in order to improve the performance in this research field.

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\footnote{https://github.com/hiepnh137/SemEval2023-Task6-Rhetorical-Roles}.