  We cast neural machine translation (NMT) as a classification task in an
autoregressive setting and analyze the limitations of both classification and
autoregression components. Classifiers are known to perform better with
balanced class distributions during training. Since the Zipfian nature of
languages causes imbalanced classes, we explore its effect on NMT. We analyze
the effect of various vocabulary sizes on NMT performance on multiple languages
with many data sizes, and reveal an explanation for why certain vocabulary
sizes are better than others.
