This way, RAML not only can incorporate the task metric information into training, but it can also alleviate the exposure bias by exposing imperfect outputs to the model.
with the particular choice of target distribution, RAML not only makes sure the ground-truth reference remains the mode, but also allows the model to explore sequences that are not exactly the same as the reference but have relatively high rewards.