  Prior work has explored directly regularizing the output distributions of
probabilistic models to alleviate peaky (i.e. over-confident) predictions, a
common sign of overfitting. This class of techniques, of which label smoothing
is one, has a connection to entropy regularization. Despite the consistent
success of label smoothing across architectures and data sets in language
generation tasks, two problems remain open: (1) there is little understanding
of the underlying effects entropy regularizers have on models, and (2) the full
space of entropy regularization techniques is largely unexplored. We introduce
a parametric family of entropy regularizers, which includes label smoothing as
a special case, and use it to gain a better understanding of the relationship
between the entropy of a model and its performance on language generation
tasks. We also find that variance in model performance can be explained largely
by the resulting entropy of the model. Lastly, we find that label smoothing
provably does not allow for sparsity in an output distribution, an undesirable
property for language generation models, and therefore advise the use of other
entropy regularization methods in its place.
