Machine translation has become an irreplaceable application in the use of mobile phones. However, the current mainstream neural machine translation models depend on continuously increasing the amount of parameters to achieve better performance, which is not applicable to the mobile phone. In this paper, we improve the performance of neural machine translation (NMT) with shallow syntax (e.g., POS tag) of target language, which has better accuracy and latency than deep syntax such as dependency parsing. In particular, our models take less parameters and runtime than other complex machine translation models, making mobile applications possible. In detail, we present three RNN-based NMT decoding models (independent decoder, gates shared decoder and fully shared decoder) to jointly predict target word and POS tag sequences. Experiments on Chinese-English and German-English translation tasks show that the fully shared decoder can acquire the best performance, which increases the BLEU score by 1.4 and 2.25 points respectively compared with the attention-based NMT model. In addition, we extend the idea to transformer-based models, and the experimental results also show that the BLEU score is further improved.

机器翻译已成为手机使用中不可替代的应用程序。然而，当前主流的神经机器翻译模型依赖于不断增加的参数数量以实现更好的性能，这不适用于手机。在本文中，我们使用目标语言的浅语法（例如POS标签）提高了神经机器翻译（NMT）的性能，该算法的准确性和延迟比依赖项解析之类的深语法更好。特别是，与其他复杂的机器翻译模型相比，我们的模型需要较少的参数和运行时间，从而使移动应用程序成为可能。详细地，我们提出了三种基于RNN的NMT解码模型（独立解码器，门共享解码器和完全共享解码器），以共同预测目标词和POS标签序列。通过汉英和德英翻译任务的实验表明，完全共享的解码器可以获得最佳性能，与基于注意力的NMT模型相比，其BLEU得分分别提高了1.4和2.25分。此外，我们将该思想扩展到基于变压器的模型，并且实验结果还表明，BLEU分数得到了进一步提高。