  Neural machine translation (NMT) usually works in a seq2seq learning way by
viewing either source or target sentence as a linear sequence of words, which
can be regarded as a special case of graph, taking words in the sequence as
nodes and relationships between words as edges. In the light of the current NMT
models more or less capture graph information among the sequence in a latent
way, we present a graph-to-sequence model facilitating explicit graph
information capturing. In detail, we propose a graph-based SAN-based NMT model
called Graph-Transformer by capturing information of subgraphs of different
orders in every layers. Subgraphs are put into different groups according to
their orders, and every group of subgraphs respectively reflect different
levels of dependency between words. For fusing subgraph representations, we
empirically explore three methods which weight different groups of subgraphs of
different orders. Results of experiments on WMT14 English-German and IWSLT14
German-English show that our method can effectively boost the Transformer with
an improvement of 1.1 BLEU points on WMT14 English-German dataset and 1.0 BLEU
points on IWSLT14 German-English dataset.
