  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.
