  The growth of pending legal cases in populous countries, such as India, has
become a major issue. Developing effective techniques to process and understand
legal documents is extremely useful in resolving this problem. In this paper,
we present our systems for SemEval-2023 Task 6: understanding legal texts (Modi
et al., 2023). Specifically, we first develop the Legal-BERT-HSLN model that
considers the comprehensive context information in both intra- and
inter-sentence levels to predict rhetorical roles (subtask A) and then train a
Legal-LUKE model, which is legal-contextualized and entity-aware, to recognize
legal entities (subtask B). Our evaluations demonstrate that our designed
models are more accurate than baselines, e.g., with an up to 15.0% better F1
score in subtask B. We achieved notable performance in the task leaderboard,
e.g., 0.834 micro F1 score, and ranked No.5 out of 27 teams in subtask A.
