@inproceedings{zahid-etal-2024-multi,
    title = "Multi-Loss Fusion: Angular and Contrastive Integration for Machine-Generated Text Detection",
    author = "Zahid, Iqra  and
      Chang, Yue  and
      Madusanka, Tharindu  and
      Sun, Youcheng  and
      Batista-Navarro, Riza",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2024.findings-emnlp.421/",
    doi = "10.18653/v1/2024.findings-emnlp.421",
    pages = "7189--7202",
    abstract = "Modern natural language generation (NLG) systems have led to the development of synthetic human-like open-ended texts, posing concerns as to who the original author of a text is. To address such concerns, we introduce DeB-Ang: the utilisation of a custom DeBERTa model with angular loss and contrastive loss functions for effective class separation in neural text classification tasks. We expand the application of this model on binary machine-generated text detection and multi-class neural authorship attribution. We demonstrate improved performance on many benchmark datasets whereby the accuracy for machine-generated text detection was increased by as much as 38.04{\%} across all datasets."
}