NLP-LISAC at SemEval-2024 Task 1: Transformer-based approaches for Determining Semantic Textual Relatedness

Abdessamad Benlahbib, Anass Fahfouh, Hamza Alami, Achraf Boumhidi


Abstract
This paper presents our system and findings for SemEval 2024 Task 1 Track A Supervised Semantic Textual Relatedness. The main objective of this task was to detect the degree of semantic relatedness between pairs of sentences. Our submitted models (ranked 6/24 in Algerian Arabic, 7/25 in Spanish, 12/23 in Moroccan Arabic, and 13/36 in English) consist of various transformer-based models including MARBERT-V2, mDeBERTa-V3-Base, DarijaBERT, and DeBERTa-V3-Large, fine-tuned using different loss functions including Huber Loss, Mean Absolute Error, and Mean Squared Error.
Anthology ID:
2024.semeval-1.33
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:
213–217
Language:
URL:
https://aclanthology.org/2024.semeval-1.33
DOI:
10.18653/v1/2024.semeval-1.33
Bibkey:
Cite (ACL):
Abdessamad Benlahbib, Anass Fahfouh, Hamza Alami, and Achraf Boumhidi. 2024. NLP-LISAC at SemEval-2024 Task 1: Transformer-based approaches for Determining Semantic Textual Relatedness. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 213–217, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
NLP-LISAC at SemEval-2024 Task 1: Transformer-based approaches for Determining Semantic Textual Relatedness (Benlahbib et al., SemEval 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.semeval-1.33.pdf
Supplementary material:
 2024.semeval-1.33.SupplementaryMaterial.txt