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

Why Neural Machine Translation Prefers Empty Outputs