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Generating a textual description from a set of RDF triplets is a challenging task in natural language generation. Recent neural methods have become the mainstream for this task, which often generate sentences from scratch. However, due to the huge gap between the structured input and the unstructured output, the input triples alone are insufficient to decide an expressive and specific description. In this paper, we propose a novel anchor-to-prototype framework to bridge the gap betweendoi:10.24963/ijcai.2020/519 dblp:conf/ijcai/ChenLHQ20 fatcat:jomkkkx42bdwtallumrfgvthwm