Abstract
This paper summarizes Team SCaLAR’s work on SemEval-2024 Task 5: Legal Argument Reasoning in Civil Procedure. To address this Binary Classification task, which was daunting due to the complexity of the Legal Texts involved, we propose a simple yet novel similarity and distance-based unsupervised approach to generate labels. Further, we explore the Multi-level fusion of Legal-Bert embeddings using ensemble features, including CNN, GRU, and LSTM. To address the lengthy nature of Legal explanation in the dataset, we introduce T5-based segment-wise summarization, which successfully retained crucial information, enhancing the model’s performance. Our unsupervised system witnessed a 20-point increase in macro F1-score on the development set and a 10-point increase on the test set, which is promising given its uncomplicated architecture.- Anthology ID:
- 2024.semeval-1.30
- Volume:
- Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Harish Tayyar Madabushi, Giovanni Da San Martino, Sara Rosenthal, Aiala Rosá
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 193–199
- Language:
- URL:
- https://aclanthology.org/2024.semeval-1.30
- DOI:
- Cite (ACL):
- Manvith Prabhu, Haricharana Srinivasa, and Anand Kumar. 2024. SCaLAR NITK at SemEval-2024 Task 5: Towards Unsupervised Question Answering system with Multi-level Summarization for Legal Text. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 193–199, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- SCaLAR NITK at SemEval-2024 Task 5: Towards Unsupervised Question Answering system with Multi-level Summarization for Legal Text (Prabhu et al., SemEval 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.semeval-1.30.pdf