  Syntax has been proven to be remarkably effective in neural machine
translation (NMT). Previous models obtained syntax information from syntactic
parsing tools and integrated it into NMT models to improve translation
performance. In this work, we propose a method to incorporate syntax
information into a complex-valued Encoder-Decoder architecture. The proposed
model jointly learns word-level and syntax-level attention scores from the
source side to the target side using an attention mechanism. Importantly, it is
not dependent on specific network architectures and can be directly integrated
into any existing sequence-to-sequence (Seq2Seq) framework. The experimental
results demonstrate that the proposed method can bring significant improvements
in BLEU scores on two datasets. In particular, the proposed method achieves a
greater improvement in BLEU scores in translation tasks involving language
pairs with significant syntactic differences.
