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Why Neural Machine Translation Prefers Empty Outputs

2020-12-24 22:25:22
Xing Shi, Yijun Xiao, Kevin Knight
arXiv_CL
     
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2012.13454

PDF

https://arxiv.org/pdf/2012.13454.pdf

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