@inproceedings{ebrahimi-etal-2024-sharif,
title = "Sharif-{MGTD} at {S}em{E}val-2024 Task 8: A Transformer-Based Approach to Detect Machine Generated Text",
author = "Ebrahimi, Seyedeh Fatemeh and
Akhavan Azari, Karim and
Iravani, Amirmasoud and
Qazvini, Arian and
Sadeghi, Pouya and
Taghavi, Zeinab and
Sameti, Hossein",
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-1.85",
pages = "565--572",
abstract = "In this paper, we delve into the realm of detecting machine-generated text (MGT) within Natural Language Processing (NLP). Our approach involves fine-tuning a RoBERTa-base Transformer, a robust neural architecture, to tackle MGT detection as a binary classification task. Specifically focusing on Subtask A (Monolingual - English) within the SemEval-2024 competition framework, our system achieves a 78.9{\%} accuracy on the test dataset, placing us 57th among participants. While our system demonstrates proficiency in identifying human-written texts, it faces challenges in accurately discerning MGTs.",
}
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%0 Conference Proceedings
%T Sharif-MGTD at SemEval-2024 Task 8: A Transformer-Based Approach to Detect Machine Generated Text
%A Ebrahimi, Seyedeh Fatemeh
%A Akhavan Azari, Karim
%A Iravani, Amirmasoud
%A Qazvini, Arian
%A Sadeghi, Pouya
%A Taghavi, Zeinab
%A Sameti, Hossein
%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 ebrahimi-etal-2024-sharif
%X In this paper, we delve into the realm of detecting machine-generated text (MGT) within Natural Language Processing (NLP). Our approach involves fine-tuning a RoBERTa-base Transformer, a robust neural architecture, to tackle MGT detection as a binary classification task. Specifically focusing on Subtask A (Monolingual - English) within the SemEval-2024 competition framework, our system achieves a 78.9% accuracy on the test dataset, placing us 57th among participants. While our system demonstrates proficiency in identifying human-written texts, it faces challenges in accurately discerning MGTs.
%U https://aclanthology.org/2024.semeval-1.85
%P 565-572
Markdown (Informal)
[Sharif-MGTD at SemEval-2024 Task 8: A Transformer-Based Approach to Detect Machine Generated Text](https://aclanthology.org/2024.semeval-1.85) (Ebrahimi et al., SemEval 2024)
ACL