uOttawa at SemEval-2023 Task 6: Deep Learning for Legal Text Understanding
Intisar Almuslim, Sean Stilwell, Surya Kiran Suresh, Diana Inkpen
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.- Anthology ID:
- 2023.semeval-1.79
- Volume:
- Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 580–588
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.79
- DOI:
- 10.18653/v1/2023.semeval-1.79
- Cite (ACL):
- Intisar Almuslim, Sean Stilwell, Surya Kiran Suresh, and Diana Inkpen. 2023. uOttawa at SemEval-2023 Task 6: Deep Learning for Legal Text Understanding. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 580–588, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- uOttawa at SemEval-2023 Task 6: Deep Learning for Legal Text Understanding (Almuslim et al., SemEval 2023)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2023.semeval-1.79.pdf