Cornelia Genz
2024
Team art-nat-HHU at SemEval-2024 Task 8: Stylistically Informed Fusion Model for MGT-Detection
Vittorio Ciccarelli
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Cornelia Genz
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Nele Mastracchio
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Wiebke Petersen
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Anna Stein
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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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