Neural Machine Translation (NMT) generates target words sequentially in the
way of predicting the next word conditioned on the context words. At training
time, it predicts with the ground truth words as context while at inference it
has to generate the entire sequence from scratch. This discrepancy of the fed
context leads to error accumulation among the way. Furthermore, word-level
training requires strict matching between the generated sequence and the ground
truth sequence which leads to overcorrection over different but reasonable
translations. In this paper, we address these issues by sampling context words
not only from the ground truth sequence but also from the predicted sequence by
the model during training, where the predicted sequence is selected with a
sentence-level optimum. Experiment results on Chinese->English and WMT'14
English->German translation tasks demonstrate that our approach can achieve
significant improvements on multiple datasets.

本研究针对神经机器翻译存在的训练误差积累和过度校正问题，提出了一种从训练模型的预测序列中采样上下文词并使用句子级优化方法选择预测序列的训练策略，并在中英互译和 WMT'14 英德互译数据集上得到了显著提高。