Abstract
In this paper we present the system for Track A in the SemEval-2024 Task 1: Semantic Textual Relatedness for African and Asian Languages (STR). The proposed system integrates a Siamese Network architecture with pre-trained language models, including BERT, RoBERTa, and the Universal Sentence Encoder (USE). Through rigorous experimentation and analysis, we evaluate the performance of these models across multiple languages. Our findings reveal that the Universal Sentence Encoder excels in capturing semantic similarities, outperforming BERT and RoBERTa in most scenarios. Particularly notable is the USE’s exceptional performance in English and Marathi. These results emphasize the importance of selecting appropriate pre-trained models based on linguistic considerations and task requirements.- Anthology ID:
- 2024.semeval-1.138
- 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:
- 959–963
- Language:
- URL:
- https://aclanthology.org/2024.semeval-1.138
- DOI:
- Cite (ACL):
- Yasamin Aali, Sardar Hamidian, and Parsa Farinneya. 2024. YSP at SemEval-2024 Task 1: Enhancing Sentence Relatedness Assessment using Siamese Networks. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 959–963, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- YSP at SemEval-2024 Task 1: Enhancing Sentence Relatedness Assessment using Siamese Networks (Aali et al., SemEval 2024)
- PDF:
- https://preview.aclanthology.org/ingestion-checklist/2024.semeval-1.138.pdf