  Previous studies show that incorporating external information could improve
the translation quality of Neural Machine Translation (NMT) systems. However,
there are inevitably noises in the external information, severely reducing the
benefit that the existing methods could receive from the incorporation. To
tackle the problem, this study pays special attention to the discrimination of
the noises during the incorporation. We argue that there exist two kinds of
noise in this external information, i.e. global noise and local noise, which
affect the translations for the whole sentence and for some specific words,
respectively. Accordingly, we propose a general framework that learns to
jointly discriminate both the global and local noises, so that the external
information could be better leveraged. Our model is trained on the dataset
derived from the original parallel corpus without any external labeled data or
annotation. Experimental results in various real-world scenarios, language
pairs, and neural architectures indicate that discriminating noises contributes
to significant improvements in translation quality by being able to better
incorporate the external information, even in very noisy conditions.
