ResearchTeam_HCN at SemEval-2023 Task 6: A knowledge enhanced transformers based legal NLP system

Abstract

This paper presents our work on LegalEval (understanding legal text), one of the tasks in SemEval-2023. It comprises of three sub-tasks namely Rhetorical Roles (RR), Legal Named Entity Recognition (L-NER), and Court Judgement Prediction with Explanation (CJPE). We developed different deep-learning models for each sub-tasks. For RR, we developed a multitask learning model with contextual sequential sentence classification as the main task and non-contextual single sentence prediction as the secondary task. Our model achieved an F1-score of 76.50% on the unseen test set, and we attained the 14th position on the leaderboard. For the L-NER problem, we have designed a hybrid model, consisting of a multi-stage knowledge transfer learning framework and a rule-based system. This model achieved an F1-score of 91.20% on the blind test set and attained the top position on the final leaderboard. Finally, for the CJPE task, we used a hierarchical approach and could get around 66.67% F1-score on judgment prediction and 45.83% F1-score on the explainability of the CJPE task, and we attained 8th position on the leaderboard for this sub-task.