@inproceedings{jin-wang-2023-teamshakespeare,
    title = "{T}eam{S}hakespeare at {S}em{E}val-2023 Task 6: Understand Legal Documents with Contextualized Large Language Models",
    author = "Jin, Xin  and
      Wang, Yuchen",
    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-1.72",
    doi = "10.18653/v1/2023.semeval-1.72",
    pages = "517--525",
    abstract = "The growth of pending legal cases in populouscountries, such as India, has become a major is-sue. Developing effective techniques to processand understand legal documents is extremelyuseful in resolving this problem. In this pa-per, we present our systems for SemEval-2023Task 6: understanding legal texts (Modi et al., 2023). Specifically, we first develop the Legal-BERT-HSLN model that considers the com-prehensive context information in both intra-and inter-sentence levels to predict rhetoricalroles (subtask A) and then train a Legal-LUKEmodel, which is legal-contextualized and entity-aware, to recognize legal entities (subtask B).Our evaluations demonstrate that our designedmodels are more accurate than baselines, e.g.,with an up to 15.0{\%} better F1 score in subtaskB. We achieved notable performance in the taskleaderboard, e.g., 0.834 micro F1 score, andranked No.5 out of 27 teams in subtask A.",
}

The growth of pending legal cases in populouscountries, such as India, has become a major is-sue. Developing effective techniques to processand understand legal documents is extremelyuseful in resolving this problem. In this pa-per, we present our systems for SemEval-2023Task 6: understanding legal texts (Modi et al., 2023). Specifically, we first develop the Legal-BERT-HSLN model that considers the com-prehensive context information in both intra-and inter-sentence levels to predict rhetoricalroles (subtask A) and then train a Legal-LUKEmodel, which is legal-contextualized and entity-aware, to recognize legal entities (subtask B).Our evaluations demonstrate that our designedmodels are more accurate than baselines, e.g.,with an up to 15.0{\%} better F1 score in subtaskB. We achieved notable performance in the taskleaderboard, e.g., 0.834 micro F1 score, andranked No.5 out of 27 teams in subtask A.
Jin, Xin Wang, Yuchen
TeamShakespeare at SemEval-2023 Task 6: Understand Legal Documents with Contextualized Large Language Models