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 ========== èéáğö