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 addresses this exposure bias by gradually transitioning from using ground truth inputs to using the model's own predictions during training. At the beginning of training, the model is trained using the ground truth inputs, just like in standard training. As training progresses, the model is increasingly trained using its own predictions. This is controlled by a schedule, which determines the probability of using the ground truth input at each step. The schedule typically starts with a high probability of using the ground truth and gradually decreases this probability over time. This allows the model to gradually adapt to the challenges of using its own predictions as input. Several different schedules can be used, such as a linear schedule, an exponential schedule, or a sigmoid schedule. The choice of schedule depends on the specific task and the model architecture. Scheduled sampling can improve the performance of neural machine translation models by making them more robust to their own mistakes and by reducing the discrepancy between training and inference conditions. For instance, early in training the model might translate "The cat sat" as "Le chat ass", and during scheduled sampling, the model might be forced to try to translate knowing that it previously generated "Le chat ass" even if it isn't perfectly correct. This can lead to more accurate and fluent translations.