  Incorporating tagging into neural machine translation (NMT) systems has shown
promising results in helping translate rare words such as named entities (NE).
However, translating NE in low-resource setting remains a challenge. In this
work, we investigate the effect of using tags and NE hypernyms from knowledge
graphs (KGs) in parallel corpus in different levels of resource conditions. We
find the tag-and-copy mechanism (tag the NEs in the source sentence and copy
them to the target sentence) improves translation in high-resource settings
only. Introducing copying also results in polarizing effects in translating
different parts-of-speech (POS). Interestingly, we find that copy accuracy for
hypernyms is consistently higher than that of entities. As a way of avoiding
"hard" copying and utilizing hypernym in bootstrapping rare entities, we
introduced a "soft" tagging mechanism and found consistent improvement in high
and low-resource settings.
