@inproceedings{wei-2024-team,
    title = "Team {AT} at {S}em{E}val-2024 Task 8: Machine-Generated Text Detection with Semantic Embeddings",
    author = "Wei, Yuchen",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Tayyar Madabushi, Harish  and
      Da San Martino, Giovanni  and
      Rosenthal, Sara  and
      Ros{\'a}, Aiala},
    booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.semeval-1.75/",
    doi = "10.18653/v1/2024.semeval-1.75",
    pages = "492--496",
    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."
}