GCP: Graph Encoder With Content-Planning for Sentence Generation From Knowledge Bases - Trisedya, Bayu Distiawan; Qi, Jianzhong; Wang, Wei; Zhang, Rui (IEEE transactions on pattern analysis and machine intelligence, 2022)
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 G raph 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% 3.6%<inline-graphic xlink:href="zhang-ieq1-3118703.gif"/> , 4.1% 4.1%<inline-graphic xlink:href="zhang-ieq2-3118703.gif"/> , and 3.8% 3.8%<inline-graphic xlink:href="zhang-ieq3-3118703.gif"/> in three common metrics BLEU, METEOR, and TER, respectively. The code is available at ( https://github.com/ruizhang-ai/GCP/ )