Neural machine translation models usually adopt the teacher forcing strategy
for training which requires the predicted sequence matches ground truth word by
word and forces the probability of each prediction to approach a 0-1
distribution. However, the strategy casts all the portion of the distribution
to the ground truth word and ignores other words in the target vocabulary even
when the ground truth word cannot dominate the distribution. To address the
problem of teacher forcing, we propose a method to introduce an evaluation
module to guide the distribution of the prediction. The evaluation module
accesses each prediction from the perspectives of fluency and faithfulness to
encourage the model to generate the word which has a fluent connection with its
past and future translation and meanwhile tends to form a translation
equivalent in meaning to the source. The experiments on multiple translation
tasks show that our method can achieve significant improvements over strong
baselines.

论文提出了一种引入评估模块来指导预测结果分布的神经机器翻译方法，从流畅性和忠实度的角度引导模型生成具有连接性的短语，实验结果表明，该方法可以显著提高翻译质量。