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Consider the following code snippet used for creating a multimodal dataset with PyTorch. The dataset contains images and corresponding text descriptions. However, during training, you observe a significant imbalance in the data distribution of text lengths. Which of the following techniques would BEST address this issue?
Correct Answer: B
Padding or truncating text sequences to a fixed length is a standard technique for handling variable-length sequences in NLP tasks. This ensures that all text inputs have the same dimensionality, which is required for efficient batch processing in neural networks- While image augmentation can improve the model's robustness to variations in image data, it does not directly address the issue of text length imbalance. Learning rate scheduling and batch normalization are general training techniques that can improve convergence, but they do not specifically address the text length imbalance.