Creative storytelling with language models and knowledge graphs
Year: 2020
Type: article
Abstract: Automated story generation is a popular and well-recognized task in the field of natural language processing. The emergence of pre-trained language models based on large Transformer architectures shows the great capability of text generation. However, language models are limited when the generation requires explicit clues within the context. In this research, we study how to combine knowledge graphs with language models, and build a creative story generation system named DICE. DICE uses external knowledge graphs to provide context clues and implicit knowledge to generate coherent and creative stories. The evaluation shows that our approach can effectively inject the knowledge from knowledge graphs into the stories automatically generated by the language model.
Source: VU Research Portal
Authors: Xinran YANG, Ilaria Tiddi
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3095631487