  Neural machine translation (NMT) systems operate primarily on words (or
sub-words), ignoring lower-level patterns of morphology. We present a
character-aware decoder designed to capture such patterns when translating into
morphologically rich languages. We achieve character-awareness by augmenting
both the softmax and embedding layers of an attention-based encoder-decoder
model with convolutional neural networks that operate on the spelling of a
word. To investigate performance on a wide variety of morphological phenomena,
we translate English into 14 typologically diverse target languages using the
TED multi-target dataset. In this low-resource setting, the character-aware
decoder provides consistent improvements with BLEU score gains of up to
$+3.05$. In addition, we analyze the relationship between the gains obtained
and properties of the target language and find evidence that our model does
indeed exploit morphological patterns.
