SubmissionNumber#=%=#60
FinalPaperTitle#=%=#All-Mpnet at SemEval-2024 Task 1: Application of Mpnet for Evaluating Semantic Textual Relatedness
ShortPaperTitle#=%=#
NumberOfPages#=%=#6
CopyrightSigned#=%=#Marco Siino
JobTitle#==#
Organization#==#University of Catania
Abstract#==#In this study, we tackle the task of automatically discerning the level of semantic relatedness between pairs of sentences. Specifically, Task 1 at SemEval-2024 involves predicting the Semantic Textual Relatedness (STR) of sentence pairs. Participants are tasked with ranking sentence pairs based on their proximity in meaning, quantified by their degree of semantic relatedness, across 14 different languages. Each sentence pair is assigned manually determined relatedness scores ranging from 0 (indicating complete lack of relation) to 1 (denoting maximum relatedness). In our submitted approach on the official test set, focusing on Task 1 (a supervised task in English and Spanish), we achieve a Spearman rank correlation coefficient of 0.808 for the English language and 0.611 for the Spanish language.
Author{1}{Firstname}#=%=#Marco
Author{1}{Lastname}#=%=#Siino
Author{1}{Username}#=%=#marcosiino
Author{1}{Email}#=%=#marco.siino@unipa.it
Author{1}{Affiliation}#=%=#Università degli Studi di Catania

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