Automated Text Identification Using CNN and Training Dynamics

• URL: http://arxiv.org/abs/2405.11212v1
• Date: Sat, 18 May 2024 07:37:17 GMT
• Title: Automated Text Identification Using CNN and Training Dynamics
• Authors: Claudiu Creanga, Liviu Petrisor Dinu,
• Abstract summary: 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 found that training the model only on a subset of ambiguous examples improves the model's out-of-distribution generalization.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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