In this paper, we address the problem of inadequate translation by introducing a novel sentence alignment constrain under an adversarial training framework (Goodfellow et al., 2014;Lu et al., 2017;Yang et al., 2018).
our approach contains two sub-models: i) a sentence alignment oriented discriminator D learns to estimate the alignment score and sort the translation candidates by mainly considering the weighted alignment pairs (Ma, 2006) at a sentence representation level; and ii) a standard NMT model G aims to produce an appropriate translation with the highest ranking score (assigned by D) in the candidate list.