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:
- 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)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.semeval-1.242.pdf