Syntax has been demonstrated highly effective in neural machine translation
(NMT). Previous NMT models integrate syntax by representing 1-best tree outputs
from a well-trained parsing system, e.g., the representative Tree-RNN and
Tree-Linearization methods, which may suffer from error propagation. In this
work, we propose a novel method to integrate source-side syntax implicitly for
NMT. The basic idea is to use the intermediate hidden representations of a
well-trained end-to-end dependency parser, which are referred to as
syntax-aware word representations (SAWRs). Then, we simply concatenate such
SAWRs with ordinary word embeddings to enhance basic NMT models. The method can
be straightforwardly integrated into the widely-used sequence-to-sequence
(Seq2Seq) NMT models. We start with a representative RNN-based Seq2Seq baseline
system, and test the effectiveness of our proposed method on two benchmark
datasets of the Chinese-English and English-Vietnamese translation tasks,
respectively. Experimental results show that the proposed approach is able to
bring significant BLEU score improvements on the two datasets compared with the
baseline, 1.74 points for Chinese-English translation and 0.80 point for
English-Vietnamese translation, respectively. In addition, the approach also
outperforms the explicit Tree-RNN and Tree-Linearization methods.

本研究提出了一种基于依赖解析器的隐式语法表示方法，通过使用中间隐藏的依赖解析器表示来增强基础神经机器翻译模型，可以在中英文和英越翻译任务的基准数据集上显著提高 BLEU 打分表现，并超过了显式树状 RNN 和线性化方法。