  This work systematically analyzes the smoothing effect of vocabulary
reduction for phrase translation models. We extensively compare various
word-level vocabularies to show that the performance of smoothing is not
significantly affected by the choice of vocabulary. This result provides
empirical evidence that the standard phrase translation model is extremely
sparse. Our experiments also reveal that vocabulary reduction is more effective
for smoothing large-scale phrase tables.
