Abstract
This paper presents a novel approach to legal judgment prediction by combining BERT embeddings with a Delaunay-based Graph Neural Network (GNN). Unlike inductive methods that classify legal documents independently, our transductive approach models the entire document set as a graph, capturing both contextual and relational information. This method significantly improves classification accuracy by enabling effective label propagation across connected documents. Evaluated on the Swiss-Judgment-Prediction (SJP) dataset, our model outperforms established baselines, including larger models with cross-lingual training and data augmentation techniques, while maintaining efficiency with minimal computational overhead.- Anthology ID:
- 2024.nllp-1.15
- Volume:
- Proceedings of the Natural Legal Language Processing Workshop 2024
- Month:
- November
- Year:
- 2024
- Address:
- Miami, FL, USA
- Editors:
- Nikolaos Aletras, Ilias Chalkidis, Leslie Barrett, Cătălina Goanță, Daniel Preoțiuc-Pietro, Gerasimos Spanakis
- Venue:
- NLLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 187–193
- Language:
- URL:
- https://aclanthology.org/2024.nllp-1.15
- DOI:
- 10.18653/v1/2024.nllp-1.15
- Cite (ACL):
- Hugo Attali and Nadi Tomeh. 2024. Transductive Legal Judgment Prediction Combining BERT Embeddings with Delaunay-Based GNNs. In Proceedings of the Natural Legal Language Processing Workshop 2024, pages 187–193, Miami, FL, USA. Association for Computational Linguistics.
- Cite (Informal):
- Transductive Legal Judgment Prediction Combining BERT Embeddings with Delaunay-Based GNNs (Attali & Tomeh, NLLP 2024)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.nllp-1.15.pdf