  Although Neural Machine Translation (NMT) models have advanced
state-of-the-art performance in machine translation, they face problems like
the inadequate translation. We attribute this to that the standard Maximum
Likelihood Estimation (MLE) cannot judge the real translation quality due to
its several limitations. In this work, we propose an adequacy-oriented learning
mechanism for NMT by casting translation as a stochastic policy in
Reinforcement Learning (RL), where the reward is estimated by explicitly
measuring translation adequacy. Benefiting from the sequence-level training of
RL strategy and a more accurate reward designed specifically for translation,
our model outperforms multiple strong baselines, including (1) standard and
coverage-augmented attention models with MLE-based training, and (2) advanced
reinforcement and adversarial training strategies with rewards based on both
word-level BLEU and character-level chrF3. Quantitative and qualitative
analyses on different language pairs and NMT architectures demonstrate the
effectiveness and universality of the proposed approach.
