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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 thisdoi:10.18653/v1/p19-1427 dblp:conf/acl/WietingBGN19 fatcat:ckylq5pjtbfhhpswp2lkgpplwe