  Nearly all previous work on neural machine translation (NMT) has used quite
restricted vocabularies, perhaps with a subsequent method to patch in unknown
words. This paper presents a novel word-character solution to achieving open
vocabulary NMT. We build hybrid systems that translate mostly at the word level
and consult the character components for rare words. Our character-level
recurrent neural networks compute source word representations and recover
unknown target words when needed. The twofold advantage of such a hybrid
approach is that it is much faster and easier to train than character-based
ones; at the same time, it never produces unknown words as in the case of
word-based models. On the WMT'15 English to Czech translation task, this hybrid
approach offers an addition boost of +2.1-11.4 BLEU points over models that
already handle unknown words. Our best system achieves a new state-of-the-art
result with 20.7 BLEU score. We demonstrate that our character models can
successfully learn to not only generate well-formed words for Czech, a
highly-inflected language with a very complex vocabulary, but also build
correct representations for English source words.
