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


Abstract
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%.
Anthology ID:
2024.semeval-1.242
Volume:
Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Atul Kr. Ojha, A. Seza Doğruöz, Harish Tayyar Madabushi, Giovanni Da San Martino, Sara Rosenthal, Aiala Rosá
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
1690–1697
Language:
URL:
https://aclanthology.org/2024.semeval-1.242
DOI:
10.18653/v1/2024.semeval-1.242
Bibkey:
Cite (ACL):
Vittorio Ciccarelli, Cornelia Genz, Nele Mastracchio, Wiebke Petersen, Anna Stein, and Hanxin Xia. 2024. Team art-nat-HHU at SemEval-2024 Task 8: Stylistically Informed Fusion Model for MGT-Detection. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 1690–1697, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Team art-nat-HHU at SemEval-2024 Task 8: Stylistically Informed Fusion Model for MGT-Detection (Ciccarelli et al., SemEval 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.semeval-1.242.pdf
Supplementary material:
 2024.semeval-1.242.SupplementaryMaterial.zip
Supplementary material:
 2024.semeval-1.242.SupplementaryMaterial.txt