@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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T uOttawa at SemEval-2023 Task 6: Deep Learning for Legal Text Understanding
%A Almuslim, Intisar
%A Stilwell, Sean
%A Kiran Suresh, Surya
%A Inkpen, Diana
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F almuslim-etal-2023-uottawa
%X 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.
%R 10.18653/v1/2023.semeval-1.79
%U https://aclanthology.org/2023.semeval-1.79
%U https://doi.org/10.18653/v1/2023.semeval-1.79
%P 580-588
Markdown (Informal)
[uOttawa at SemEval-2023 Task 6: Deep Learning for Legal Text Understanding](https://aclanthology.org/2023.semeval-1.79) (Almuslim et al., SemEval 2023)
ACL