@inproceedings{ningthoujam-etal-2023-researchteam,
    title = "{R}esearch{T}eam{\_}{HCN} at {S}em{E}val-2023 Task 6: A knowledge enhanced transformers based legal {NLP} system",
    author = "Ningthoujam, Dhanachandra  and
      Patel, Pinal  and
      Kareddula, Rajkamal  and
      Vangipuram, Ramanand",
    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-1.173",
    doi = "10.18653/v1/2023.semeval-1.173",
    pages = "1245--1253",
    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 Judge- ment Prediction with Explanation (CJPE). We developed different deep-learning models for each sub-tasks. For RR, we developed a multi- task learning model with contextual sequential sentence classification as the main task and non- contextual single sentence prediction as the sec- ondary task. Our model achieved an F1-score of 76.50{\%} on the unseen test set, and we at- tained 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 ap- proach 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.",
}

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 Judge- ment Prediction with Explanation (CJPE). We developed different deep-learning models for each sub-tasks. For RR, we developed a multi- task learning model with contextual sequential sentence classification as the main task and non- contextual single sentence prediction as the sec- ondary task. Our model achieved an F1-score of 76.50{\%} on the unseen test set, and we at- tained 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 ap- proach 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.
Ningthoujam, Dhanach ra Patel, Pinal Kareddula, Rajkamal Vangipuram, Raman
ResearchTeam_HCN at SemEval-2023 Task 6: A knowledge enhanced transformers based legal NLP system