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The knowledge graph (KG) stores a large amount of structural knowledge, whileit is not easy for direct human understanding. Knowledge graph-to-text(KG-to-text) generation aims to generate easy-to-understand sentences from theKG, and at the same time, maintains semantic consistency between generatedsentences and the KG. Existing KG-to-text generation methods phrase this taskas a sequence-to-sequence generation task with linearized KG as input andconsider the consistency issue of the generated texts and KG through a simpleselection between decoded sentence word and KG node word at each time step.However, the linearized KG order is commonly obtained through a heuristicsearch without data-driven optimization. In this paper, we optimize theknowledge description order prediction under the order supervision extractedfrom the caption and further enhance the consistency of the generated sentencesand KG through syntactic and semantic regularization. We incorporate thePart-of-Speech (POS) syntactic tags to constrain the positions to copy wordsfrom the KG and employ a semantic context scoring function to evaluate thesemantic fitness for each word in its local context when decoding each word inthe generated sentence. Extensive experiments are conducted on two datasets,WebNLG and DART, and achieve state-of-the-art performances.

Syntax Controlled Knowledge Graph-to-Text Generation with Order and Semantic Consistency