SubmissionNumber#=%=#34
FinalPaperTitle#=%=#NLP-LISAC at SemEval-2024 Task 1: Transformer-based approaches for Determining Semantic Textual Relatedness
ShortPaperTitle#=%=#
NumberOfPages#=%=#5
CopyrightSigned#=%=#ABDESSAMAD BENLAHBIB
JobTitle#==#
Organization#==#
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.
Author{1}{Firstname}#=%=#Abdessamad
Author{1}{Lastname}#=%=#Benlahbib
Author{1}{Username}#=%=#abdessamad_benlahbib
Author{1}{Email}#=%=#abdessamad.benlahbib@usmba.ac.ma
Author{1}{Affiliation}#=%=#FSDM
Author{2}{Firstname}#=%=#Anass
Author{2}{Lastname}#=%=#Fahfouh
Author{2}{Email}#=%=#anassfahfouh@gmail.com
Author{2}{Affiliation}#=%=#LISAC Laboratory, Faculty of Sciences Dhar EL Mehraz, USMBA, Fez, Morocco
Author{3}{Firstname}#=%=#Hamza
Author{3}{Lastname}#=%=#Alami
Author{3}{Username}#=%=#alamihamza
Author{3}{Email}#=%=#hamza.alami@um6p.ma
Author{3}{Affiliation}#=%=#School of Computer Science UM6P
Author{4}{Firstname}#=%=#Achraf
Author{4}{Lastname}#=%=#Boumhidi
Author{4}{Email}#=%=#achraf.boumhidi@usmba.ac.ma
Author{4}{Affiliation}#=%=#LISAC Laboratory, Faculty of Sciences Dhar EL Mehraz, USMBA, Fez, Morocco

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