SubmissionNumber#=%=#145
FinalPaperTitle#=%=#YSP at SemEval-2024 Task 1: Enhancing Sentence Relatedness Assessment using Siamese Networks
ShortPaperTitle#=%=#
NumberOfPages#=%=#5
CopyrightSigned#=%=#Yasamin Aali
JobTitle#==#
Organization#==#
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.
Author{1}{Firstname}#=%=#Yasamin
Author{1}{Lastname}#=%=#Aali
Author{1}{Username}#=%=#yasamin.aali
Author{1}{Email}#=%=#yasamin.aali01@gmail.com
Author{1}{Affiliation}#=%=#Alzahra University
Author{2}{Firstname}#=%=#Sardar
Author{2}{Lastname}#=%=#Hamidian
Author{2}{Username}#=%=#sardar
Author{2}{Email}#=%=#sardar_hamidian@comcast.com
Author{2}{Affiliation}#=%=#GWU
Author{3}{Firstname}#=%=#Parsa
Author{3}{Lastname}#=%=#Farinneya
Author{3}{Username}#=%=#parsafar
Author{3}{Email}#=%=#parsa.farinneya@mail.utoronto.ca
Author{3}{Affiliation}#=%=#University of Toronto

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