SubmissionNumber#=%=#76
FinalPaperTitle#=%=#Team AT at SemEval-2024 Task 8: Machine-Generated Text Detection with Semantic Embeddings
ShortPaperTitle#=%=#
NumberOfPages#=%=#5
CopyrightSigned#=%=#Yuchen Wei
JobTitle#==#
Organization#==#Department of Computer Science at St. Francis Xavier University
University Ave, Antigonish NS B2G2W5 Canada
Abstract#==#This study investigates the detection of machine-generated text using several semantic embedding techniques, a critical issue in the era of advanced language models. Different methodologies were examined: GloVe embeddings, N-gram embedding models, Sentence BERT, and a concatenated embedding approach, against a fine-tuned RoBERTa baseline. The research was conducted within the framework of SemEval-2024 Task 8, encompassing tasks for binary and multi-class classification of machine-generated text.
Author{1}{Firstname}#=%=#Yuchen
Author{1}{Lastname}#=%=#Wei
Author{1}{Username}#=%=#yuchenwei
Author{1}{Email}#=%=#x2020fct@stfx.ca
Author{1}{Affiliation}#=%=#St. Francis Xavier University

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