Automated Text Identification Using CNN and Training Dynamics
<|reference_start|>Automated Text Identification Using CNN and Training Dynamics: We used Data Maps to model and characterize the AuTexTification dataset. This provides insights about the behaviour of individual samples during training across epochs (training dynamics). We characterized the samples across 3 dimensions: confidence, variability and correctness. This shows the presence of 3 regions: easy-to-learn, ambiguous and hard-to-learn examples. We used a classic CNN architecture and found out that training the model only on a subset of ambiguous examples improves the model's out-of-distribution generalization.<|reference_end|>
@article{creanga2024automated,
    title={Automated Text Identification Using CNN and Training Dynamics},
    author={Claudiu Creanga, Liviu Petrisor Dinu},
    journal={Vol-3496, 2023, 4-8},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11212},
    primaryClass={cs.CL}
}