The dominant graph-to-sequence transduction models employ graph neural
networks for graph representation learning, where the structural information is
reflected by the receptive field of neurons. Unlike graph neural networks that
restrict the information exchange between immediate neighborhood, we propose a
new model, known as Graph Transformer, that uses explicit relation encoding and
allows direct communication between two distant nodes. It provides a more
efficient way for global graph structure modeling. Experiments on the
applications of text generation from Abstract Meaning Representation (AMR) and
syntax-based neural machine translation show the superiority of our proposed
model. Specifically, our model achieves 27.4 BLEU on LDC2015E86 and 29.7 BLEU
on LDC2017T10 for AMR-to-text generation, outperforming the state-of-the-art
results by up to 2.2 points. On the syntax-based translation tasks, our model
establishes new single-model state-of-the-art BLEU scores, 21.3 for
English-to-German and 14.1 for English-to-Czech, improving over the existing
best results, including ensembles, by over 1 BLEU.

本文提出了一种名为 “图变压器” 的模型，使用显式关系编码并允许两个远程节点之间的直接通信。与局部邻域信息交换的图神经网络不同，它提供了一种更有效的全局图结构建模方式，并在抽象意义表示文本生成和基于句法的神经机器翻译应用中表现出优异的性能。