Knowledge graph based natural language generation with adapted pointer-generator networks

Abstract Pointer-generator networks have recently shown superior performance in Natural Language Generation (NLG) tasks, such as automatic generating descriptions for entities in Knowledge Graph (KG). In the absence of the introductory description about an entity we intend to know, the natural language text automatically generated by neural network model can greatly help people to better understand the entity. Entities in KG always have multiple property fields and corresponding values. The target generated description sentences should consist of these slot types and slot values in a consistent and high coverage manner. In order to cover the facts in an input KG, pointer-generator networks always copy certain segments from the input sequences via softmax pointing while generating novel words through the generator. But the challenges that when and where to integrate the copied information with the generated one, how to prevent duplicate generation and information loss are hard to conquer. In this paper, the KG2TEXT model based on adapted pointer-generator networks is proposed. In it, a varied coverage loss function is devised to cover attribute-value pairs as many as possible when generating natural language descriptions for entities in KG. Secondly, an attention mechanism named supervised attention mechanism is added to the model which aims to guide the soft switch transformation process (to generate or to copy). With empirical experiments on two kinds of realistic datasets, the KG2TEXT model achieves promising results and outperforms the state-of-the-art approaches.