@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://aclanthology.org/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.",
}
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%0 Conference Proceedings
%T Team AT at SemEval-2024 Task 8: Machine-Generated Text Detection with Semantic Embeddings
%A Wei, Yuchen
%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 wei-2024-team
%X 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.
%R 10.18653/v1/2024.semeval-1.75
%U https://aclanthology.org/2024.semeval-1.75
%U https://doi.org/10.18653/v1/2024.semeval-1.75
%P 492-496
Markdown (Informal)
[Team AT at SemEval-2024 Task 8: Machine-Generated Text Detection with Semantic Embeddings](https://aclanthology.org/2024.semeval-1.75) (Wei, SemEval 2024)
ACL