  Attention mechanism, including global attention and local attention, plays a
key role in neural machine translation (NMT). Global attention attends to all
source words for word prediction. In comparison, local attention selectively
looks at fixed-window source words. However, alignment weights for the current
target word often decrease to the left and right by linear distance centering
on the aligned source position and neglect syntax-directed distance
constraints. In this paper, we extend local attention with syntax-distance
constraint, to focus on syntactically related source words with the predicted
target word, thus learning a more effective context vector for word prediction.
Moreover, we further propose a double context NMT architecture, which consists
of a global context vector and a syntax-directed context vector over the global
attention, to provide more translation performance for NMT from source
representation. The experiments on the large-scale Chinese-to-English and
English-to-Germen translation tasks show that the proposed approach achieves a
substantial and significant improvement over the baseline system.
