The standard cross-entropy loss penalizes the model whenever it fails to produce the exact word from the ground truth data used for training.
in many NLP tasks that deal with generating text from semantic representation, recovering the exact word is not necessarily optimal, and often generating a near-synonym or just a semantically close word is nearly as good or even better from the point of view of model performance.