Sharif-STR at SemEval-2024 Task 1: Transformer as a Regression Model for Fine-Grained Scoring of Textual Semantic Relations

Seyedeh Fatemeh Ebrahimi, Karim Akhavan Azari, Amirmasoud Iravani, Hadi Alizadeh, Zeinab Taghavi, Hossein Sameti


Abstract
This paper explores semantic textual relatedness (STR) using fine-tuning techniques on the RoBERTa transformer model, focusing on sentence-level STR within Track A (Supervised). The study evaluates the effectiveness of this approach across different languages, with promising results in English and Spanish but encountering challenges in Arabic.
Anthology ID:
2024.semeval-1.151
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:
1043–1052
Language:
URL:
https://aclanthology.org/2024.semeval-1.151
DOI:
10.18653/v1/2024.semeval-1.151
Bibkey:
Cite (ACL):
Seyedeh Fatemeh Ebrahimi, Karim Akhavan Azari, Amirmasoud Iravani, Hadi Alizadeh, Zeinab Taghavi, and Hossein Sameti. 2024. Sharif-STR at SemEval-2024 Task 1: Transformer as a Regression Model for Fine-Grained Scoring of Textual Semantic Relations. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 1043–1052, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Sharif-STR at SemEval-2024 Task 1: Transformer as a Regression Model for Fine-Grained Scoring of Textual Semantic Relations (Ebrahimi et al., SemEval 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.semeval-1.151.pdf
Supplementary material:
 2024.semeval-1.151.SupplementaryMaterial.txt
Supplementary material:
 2024.semeval-1.151.SupplementaryMaterial.zip