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        <title>scaLAR SemEval-2024 Task 1: Semantic Textual Relatednes for English</title>
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            <title>Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)</title>
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        <name type="personal">
            <namePart type="given">Atul</namePart>
            <namePart type="given">Kr.</namePart>
            <namePart type="family">Ojha</namePart>
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            <namePart type="given">Harish</namePart>
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    <abstract>This study investigates Semantic TextualRelated- ness (STR) within Natural LanguageProcessing (NLP) through experiments conducted on a dataset from the SemEval-2024STR task. The dataset comprises train instances with three features (PairID, Text, andScore) and test instances with two features(PairID and Text), where sentence pairs areseparated by ’/n’ in the Text column. UsingBERT(sentence transformers pipeline), we explore two approaches: one with fine-tuning(Track A: Supervised) and another without finetuning (Track B: UnSupervised). Fine-tuningthe BERT pipeline yielded a Spearman correlation coefficient of 0.803, while without finetuning, a coefficient of 0.693 was attained usingcosine similarity. The study concludes by emphasizing the significance of STR in NLP tasks,highlighting the role of pre-trained languagemodels like BERT and Sentence Transformersin enhancing semantic relatedness assessments.</abstract>
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    <identifier type="doi">10.18653/v1/2024.semeval-1.129</identifier>
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%0 Conference Proceedings
%T scaLAR SemEval-2024 Task 1: Semantic Textual Relatednes for English
%A Kumar, Anand
%A Kumar, Hemanth
%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 kumar-kumar-2024-scalar
%X This study investigates Semantic TextualRelated- ness (STR) within Natural LanguageProcessing (NLP) through experiments conducted on a dataset from the SemEval-2024STR task. The dataset comprises train instances with three features (PairID, Text, andScore) and test instances with two features(PairID and Text), where sentence pairs areseparated by ’/n’ in the Text column. UsingBERT(sentence transformers pipeline), we explore two approaches: one with fine-tuning(Track A: Supervised) and another without finetuning (Track B: UnSupervised). Fine-tuningthe BERT pipeline yielded a Spearman correlation coefficient of 0.803, while without finetuning, a coefficient of 0.693 was attained usingcosine similarity. The study concludes by emphasizing the significance of STR in NLP tasks,highlighting the role of pre-trained languagemodels like BERT and Sentence Transformersin enhancing semantic relatedness assessments.
%R 10.18653/v1/2024.semeval-1.129
%U https://aclanthology.org/2024.semeval-1.129
%U https://doi.org/10.18653/v1/2024.semeval-1.129
%P 902-906

Markdown (Informal)

[scaLAR SemEval-2024 Task 1: Semantic Textual Relatednes for English](https://aclanthology.org/2024.semeval-1.129) (Kumar & Kumar, SemEval 2024)

• scaLAR SemEval-2024 Task 1: Semantic Textual Relatednes for English (Kumar & Kumar, SemEval 2024)

ACL

• Anand Kumar and Hemanth Kumar. 2024. scaLAR SemEval-2024 Task 1: Semantic Textual Relatednes for English. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 902–906, Mexico City, Mexico. Association for Computational Linguistics.