  As a sequence-to-sequence generation task, neural machine translation (NMT)
naturally contains intrinsic uncertainty, where a single sentence in one
language has multiple valid counterparts in the other. However, the dominant
methods for NMT only observe one of them from the parallel corpora for the
model training but have to deal with adequate variations under the same meaning
at inference. This leads to a discrepancy of the data distribution between the
training and the inference phases. To address this problem, we propose
uncertainty-aware semantic augmentation, which explicitly captures the
universal semantic information among multiple semantically-equivalent source
sentences and enhances the hidden representations with this information for
better translations. Extensive experiments on various translation tasks reveal
that our approach significantly outperforms the strong baselines and the
existing methods.
