  While most neural machine translation (NMT) systems are still trained using
maximum likelihood estimation, recent work has demonstrated that optimizing
systems to directly improve evaluation metrics such as BLEU can substantially
improve final translation accuracy. However, training with BLEU has some
limitations: it doesn't assign partial credit, it has a limited range of output
values, and it can penalize semantically correct hypotheses if they differ
lexically from the reference. In this paper, we introduce an alternative reward
function for optimizing NMT systems that is based on recent work in semantic
similarity. We evaluate on four disparate languages translated to English, and
find that training with our proposed metric results in better translations as
evaluated by BLEU, semantic similarity, and human evaluation, and also that the
optimization procedure converges faster. Analysis suggests that this is because
the proposed metric is more conducive to optimization, assigning partial credit
and providing more diversity in scores than BLEU.
