The tool uses a predictive algorithm that first looks at the preceding story and then predicts the "semantic frames" - each of which represents a cluster of concepts and related knowledge - that might occur in the next 10, 100, or even 1,000 sentences in an ongoing story. Their approach, say the researchers, could help authors who are experiencing writer’s block develop the next section of novel-length works.
Unlike current automated text generated methods, the new approach could help authors to craft language for the follow-up story arc beyond the scope of a few sentences - a limitation of existing models.
“These creative writing tasks seem nearly impossible to fully automate," says Kenneth Huang, assistant professor of information sciences and technology. “The reason that we are tackling these very creative tasks is to push the boundaries of AI and natural language processing. Developing solutions for challenging creative tasks will teach us about the capacity and limitations of the current computational techniques, and so that we can further improve computer science.”
While existing models can generate a full story, they are only tested and proven to be successful on short works of 15 sentences or less. The researchers say they wanted to develop a tool that could help authors who write novels, which are typically 50,000 words or more.
“When providing longer text prediction," says Chieh-Yang Huang, doctoral student of informatics, "we essentially provide follow-up ideas to help novelists to plan their story and set up goals instead of generating detailed stories for them. We envision that in the future we can provide various ideas to stimulate novelists to brainstorm different story arcs.”
The researchers’ framework - called semantic frame forecast - breaks a long narrative down into a sequence of text blocks, each containing a fixed number of sentences. The frequency of the occurrence of each semantic frame is then calculated.
Then, the text is converted to a vector -