Training Sequence Models with Attention
Read OriginalThis technical article provides practical advice for training sequence-to-sequence models with attention mechanisms. It covers how to diagnose if a model is learning to condition on input by visualizing attention alignments and addresses the 'inference gap' caused by teacher forcing during training. The content is aimed at developers and researchers working with deep learning for sequence tasks like speech recognition or machine translation.
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