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Data-to-text (D2T) generation aims to transform structured data into naturallanguage text. Data-to-text pre-training has proved to be powerful in enhancingD2T generation and yields impressive performances. However, previouspre-training methods either oversimplified structured data into a sequencewithout considering input structures or designed training objectives tailoredfor a specific data structure (e.g., table or knowledge graph). In this paper,we unify different types of structured data (i.e., table, key-value data,knowledge graph) into the graph format and cast different data-to-textgeneration tasks as graph-to-text generation. To effectively exploit thestructural information of the input graph, we propose a structure-enhancedpre-training method for D2T generation by designing a structure-enhancedTransformer. Concretely, we devise a position matrix for the Transformer,encoding relative positional information of connected nodes in the input graph.In addition, we propose a new attention matrix to incorporate graph structuresinto the original Transformer by taking the available explicit connectivitystructure into account. Extensive experiments on six benchmark datasets showthe effectiveness of our model. Our source codes are available athttps://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/unid2t.

Unifying Structured Data as Graph for Data-to-Text Pre-Training