  We propose minimum risk training for end-to-end neural machine translation.
Unlike conventional maximum likelihood estimation, minimum risk training is
capable of optimizing model parameters directly with respect to arbitrary
evaluation metrics, which are not necessarily differentiable. Experiments show
that our approach achieves significant improvements over maximum likelihood
estimation on a state-of-the-art neural machine translation system across
various languages pairs. Transparent to architectures, our approach can be
applied to more neural networks and potentially benefit more NLP tasks.
