G2T: Generating Fluent Descriptions for Knowledge Graph

Abstract

Generating natural language descriptions for knowledge graph (KG) is an important category for intelligent writing. Recent models on this task substitute the sequence encoder in a commonly used encoder-decoder framework with a graph encoder. However, these models suffer from entity missing and repetition. In this paper, we propose a novel end-to-end generation model named G2T, which integrates a novel Graph Structure Enhanced Mechanism (GSEM) and a Copy Coverage Loss (CCL). Instead of just considering graph structure in the encoding phase in most existing methods, our GSEM fully utilizes graph structure in the decoding phase and helps to mitigate entity missing problem. Moreover, our CCL can further improve performance by avoiding generating repeated entities. With their help, our model is capable of generating fluent description for KG. The results of automatic and human evaluations show that our model outperforms the state-of-the-art models.