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        <title>All-Mpnet at SemEval-2024 Task 1: Application of Mpnet for Evaluating Semantic Textual Relatedness</title>
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            <namePart type="given">Kr.</namePart>
            <namePart type="family">Ojha</namePart>
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            <namePart type="given">Harish</namePart>
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    <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.</abstract>
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        <url>https://aclanthology.org/2024.semeval-1.59</url>
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%0 Conference Proceedings
%T All-Mpnet at SemEval-2024 Task 1: Application of Mpnet for Evaluating Semantic Textual Relatedness
%A Siino, Marco
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Tayyar Madabushi, Harish
%Y Da San Martino, Giovanni
%Y Rosenthal, Sara
%Y Rosá, Aiala
%S Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F siino-2024-mpnet
%X 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.
%U https://aclanthology.org/2024.semeval-1.59
%P 379-384

Markdown (Informal)

[All-Mpnet at SemEval-2024 Task 1: Application of Mpnet for Evaluating Semantic Textual Relatedness](https://aclanthology.org/2024.semeval-1.59) (Siino, SemEval 2024)

• All-Mpnet at SemEval-2024 Task 1: Application of Mpnet for Evaluating Semantic Textual Relatedness (Siino, SemEval 2024)

ACL

• Marco Siino. 2024. All-Mpnet at SemEval-2024 Task 1: Application of Mpnet for Evaluating Semantic Textual Relatedness. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 379–384, Mexico City, Mexico. Association for Computational Linguistics.