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In this paper, we propose an explanation of representation for self-attentionnetwork (SAN) based neural sequence encoders, which regards the informationcaptured by the model and the encoding of the model as graph structure and thegeneration of these graph structures respectively. The proposed explanationapplies to existing works on SAN-based models and can explain the relationshipamong the ability to capture the structural or linguistic information, depth ofmodel, and length of sentence, and can also be extended to other models such asrecurrent neural network based models. We also propose a revisited multigraphcalled Multi-order-Graph (MoG) based on our explanation to model the graphstructures in the SAN-based model as subgraphs in MoG and convert the encodingof SAN-based model to the generation of MoG. Based on our explanation, wefurther introduce a Graph-Transformer by enhancing the ability to capturemultiple subgraphs of different orders and focusing on subgraphs of highorders. Experimental results on multiple neural machine translation tasks showthat the Graph-Transformer can yield effective performance improvement.

To Understand Representation of Layer-aware Sequence Encoders as Multi-order-graph