In the encoder-decoder architecture for neural machine translation (NMT), the
hidden states of the recurrent structures in the encoder and decoder carry the
crucial information about the sentence.These vectors are generated by
parameters which are updated by back-propagation of translation errors through
time. We argue that propagating errors through the end-to-end recurrent
structures are not a direct way of control the hidden vectors. In this paper,
we propose to use word predictions as a mechanism for direct supervision. More
specifically, we require these vectors to be able to predict the vocabulary in
target sentence. Our simple mechanism ensures better representations in the
encoder and decoder without using any extra data or annotation. It is also
helpful in reducing the target side vocabulary and improving the decoding
efficiency. Experiments on Chinese-English and German-English machine
translation tasks show BLEU improvements by 4.53 and 1.3, respectively

本文提出了一种通过预测目标语句中词汇的方法，直接对编码器和解码器中隐藏状态向量进行监督，从而提高了表示质量和机器翻译的效率，实验证明在中英翻译任务和德英翻译任务中 BLEU 得分分别提高了 4.53 和 1.3。