Enhancing low-resource neural machine translation with syntax-graph guided self-attention

Highlights：

• We propose a syntax-aware self-attention that integrates syntactic knowledge.

• The syntactic dependency is exploited as a guidance, without any extra cost.

• The syntactic dependency is converted as a graph to combine with the NMT model.

• The syntax-aware approach also explicitly exploits sub-word units.

• We introduce multiple attention representations for stronger robustness.

• Experiments demonstrate that the approach achieves state-of-the-art results.