Vittorio Ciccarelli

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2024

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Team art-nat-HHU at SemEval-2024 Task 8: Stylistically Informed Fusion Model for MGT-Detection
Vittorio Ciccarelli | Cornelia Genz | Nele Mastracchio | Wiebke Petersen | Anna Stein | Hanxin Xia
Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)

This paper presents our solution for subtask A of shared task 8 of SemEval 2024 for classifying human- and machine-written texts in English across multiple domains. We propose a fusion model consisting of RoBERTa based pre-classifier and two MLPs that have been trained to correct the pre-classifier using linguistic features. Our model achieved an accuracy of 85%.
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