GCP: Graph Encoder with Content-Planning for Sentence Generation from Knowledge Base

A knowledge base is a large repository of facts usually represented as triples, each consisting of a subject, a predicate, and an object. The triples together form a graph, i.e., a knowledge graph . The triple representation in a knowledge graph offers a simple interface for applications to access the facts. However, this representation is not in a natural language form, which is difficult for humans to understand. We address this problem by proposing a system to translate a set of triples (i.e., a graph) into natural sentences. We take an encoder-decoder based approach. Specifically, we propose a Graph encoder with C ontent- P lanning capability ( GCP ) to encode an input graph. GCP not only works as an encoder but also serves as a content-planner by using an entity-order aware topological traversal to encode a graph. This way, GCP can capture the relationships between entities in a knowledge graph as well as providing information regarding the proper entity order for the decoder. Hence, the decoder can generate sentences with a proper entity mention ordering. Experimental results show that GCP achieves improvements over state-of-the-art models by up to $3.6\%$ , $4.1\%$ , and $3.8\%$ in three common metrics BLEU, METEOR, and TER, respectively. The code is available at ( https://github.com/ruizhang-ai/GCP/ )View less