@inproceedings{almuslim-etal-2023-uottawa,
    title = "u{O}ttawa at {S}em{E}val-2023 Task 6: Deep Learning for Legal Text Understanding",
    author = "Almuslim, Intisar  and
      Stilwell, Sean  and
      Kiran Suresh, Surya  and
      Inkpen, Diana",
    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-1.79",
    doi = "10.18653/v1/2023.semeval-1.79",
    pages = "580--588",
    abstract = "We describe the methods we used for legal text understanding, specifically Task 6 Legal-Eval at SemEval 2023. The outcomes could assist law practitioners and help automate the working process of judicial systems. The shared task defined three main sub-tasks: sub-task A, Rhetorical Roles Prediction (RR); sub-task B, Legal Named Entities Extraction (L-NER); and sub-task C, Court Judgement Prediction with Explanation (CJPE). Our team addressed all three sub-tasks by exploring various Deep Learning (DL) based models. Overall, our team{'}s approaches achieved promising results on all three sub-tasks, demonstrating the potential of deep learning-based models in the judicial domain.",
}

We describe the methods we used for legal text understanding, specifically Task 6 Legal-Eval at SemEval 2023. The outcomes could assist law practitioners and help automate the working process of judicial systems. The shared task defined three main sub-tasks: sub-task A, Rhetorical Roles Prediction (RR); sub-task B, Legal Named Entities Extraction (L-NER); and sub-task C, Court Judgement Prediction with Explanation (CJPE). Our team addressed all three sub-tasks by exploring various Deep Learning (DL) based models. Overall, our team{'}s approaches achieved promising results on all three sub-tasks, demonstrating the potential of deep learning-based models in the judicial domain.
Almuslim, Intisar Stilwell, Sean Kiran Suresh, Surya Inkpen, Diana
uOttawa at SemEval-2023 Task 6: Deep Learning for Legal Text Understanding