Rongwen Zhao


2019

bib
KB-NLG: From Knowledge Base to Natural Language Generation
Wen Cui | Minghui Zhou | Rongwen Zhao | Narges Norouzi
Proceedings of the 2019 Workshop on Widening NLP

We perform the natural language generation (NLG) task by mapping sets of Resource Description Framework (RDF) triples into text. First we investigate the impact of increasing the number of entity types in delexicalisaiton on the generation quality. Second we conduct different experiments to evaluate two widely applied language generation systems, encoder-decoder with attention and the Transformer model on a large benchmark dataset. We evaluate different models on automatic metrics, as well as the training time. To our knowledge, we are the first to apply Transformer model to this task.