SCaLAR NITK at SemEval-2024 Task 5: Towards Unsupervised Question Answering system with Multi-level Summarization for Legal Text

Manvith Prabhu, Haricharana Srinivasa, Anand Kumar


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:
10.18653/v1/2024.semeval-1.30
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2024.semeval-1.30.pdf
Supplementary material:
 2024.semeval-1.30.SupplementaryMaterial.txt
Supplementary material:
 2024.semeval-1.30.SupplementaryMaterial.zip