  We investigate why neural machine translation (NMT) systems assign high
probability to empty translations. We find two explanations. First, label
smoothing makes correct-length translations less confident, making it easier
for the empty translation to finally outscore them. Second, NMT systems use the
same, high-frequency EoS word to end all target sentences, regardless of
length. This creates an implicit smoothing that increases zero-length
translations. Using different EoS types in target sentences of different
lengths exposes and eliminates this implicit smoothing.
