SubmissionNumber#=%=#135
FinalPaperTitle#=%=#scaLAR SemEval-2024 Task 1: Semantic Textual Relatednes for English
ShortPaperTitle#=%=#
NumberOfPages#=%=#5
CopyrightSigned#=%=#Mogilipalem Hemanth Kumar
JobTitle#==#
Organization#==#
Abstract#==#This study investigates Semantic Textual
Related- ness (STR) within Natural Language
Processing (NLP) through experiments conducted on a dataset from the SemEval-2024
STR task. The dataset comprises train instances with three features (PairID, Text, and
Score) and test instances with two features
(PairID and Text), where sentence pairs are
separated by '/n' in the Text column. Using
BERT(sentence transformers pipeline), we explore two approaches: one with fine-tuning
(Track A: Supervised) and another without finetuning (Track B: UnSupervised). Fine-tuning
the BERT pipeline yielded a Spearman correlation coefficient of 0.803, while without finetuning, a coefficient of 0.693 was attained using
cosine similarity. The study concludes by emphasizing the significance of STR in NLP tasks,
highlighting the role of pre-trained language
models like BERT and Sentence Transformers
in enhancing semantic relatedness assessments.
Author{1}{Firstname}#=%=#Anand Kumar
Author{1}{Lastname}#=%=#M
Author{1}{Email}#=%=#m_anandkumar@nitk.edu.in
Author{1}{Affiliation}#=%=#NITK
Author{2}{Firstname}#=%=#Hemanth Kumar
Author{2}{Lastname}#=%=#M
Author{2}{Username}#=%=#hemanth955
Author{2}{Email}#=%=#mogilipalemhemanthkumar@gmail.com
Author{2}{Affiliation}#=%=#NITK

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