We introduce a novel multi-source technique for incorporating source syntax
into neural machine translation using linearized parses. This is achieved by
employing separate encoders for the sequential and parsed versions of the same
source sentence; the resulting representations are then combined using a
hierarchical attention mechanism. The proposed model improves over both seq2seq
and parsed baselines by over 1 BLEU on the WMT17 English-German task. Further
analysis shows that our multi-source syntactic model is able to translate
successfully without any parsed input, unlike standard parsed methods. In
addition, performance does not deteriorate as much on long sentences as for the
baselines.

提出了一种新颖的多源技术，利用线性化的解析将源语法合并到神经机器翻译中，通过使用单独的编码器将相同源语句的顺序和解析版本相结合，然后使用分层注意机制将结果表示组合，该模型在 WMT17 英德任务上比 seq2seq 和基线解析模型都有超过 1 BLEU 的改进，并且分析表明，与标准解析方法相比，我们的多源语法模型能够成功进行翻译而不需要任何已解析的输入，在长句子上的表现也不如基线模型差。