  In NMT, words are sometimes dropped from the source or generated repeatedly
in the translation. We explore novel strategies to address the coverage problem
that change only the attention transformation. Our approach allocates
fertilities to source words, used to bound the attention each word can receive.
We experiment with various sparse and constrained attention transformations and
propose a new one, constrained sparsemax, shown to be differentiable and
sparse. Empirical evaluation is provided in three languages pairs.
