Explain the concept of scheduled sampling and its potential benefits for training neural machine translation models.

Scheduled sampling is a training technique used in neural machine translation models to address the discrepancy between training and inference conditions, known as exposure bias. During training, the decoder is fed the ground truth (correct) previous words to predict the next word. However, during inference, the decoder must use its own predictions as input for the next step, potentially leading to error accumulation. If the model makes a mistake early in the translation, it can be difficult to recover, as it is never exposed to its own mistakes during training. Scheduled sampling address....