1 research outputs found
Knowledge Graph Empowered Entity Description Generation
Existing works on KG-to-text generation take as input a few RDF triples or
key-value pairs conveying the knowledge of some entities to generate a natural
language description. Existing datasets, such as WikiBIO, WebNLG, and E2E,
basically have a good alignment between an input triple/pair set and its output
text. However in practice, the input knowledge could be more than enough,
because the output description may only want to cover the most significant
knowledge. In this paper, we introduce a large-scale and challenging dataset to
facilitate the study of such practical scenario in KG-to-text. Our dataset
involves exploring large knowledge graphs (KG) to retrieve abundant knowledge
of various types of main entities, which makes the current graph-to-sequence
models severely suffered from the problems of information loss and parameter
explosion while generating the description text. We address these challenges by
proposing a multi-graph structure that is able to represent the original graph
information more comprehensively. Furthermore, we also incorporate aggregation
methods that learn to ensemble the rich graph information. Extensive
experiments demonstrate the effectiveness of our model architecture.Comment: 10 pages, 5 figure