Conveying the Predicted Future to Users: A Case Study of Story Plot
Prediction
- URL: http://arxiv.org/abs/2302.09122v1
- Date: Fri, 17 Feb 2023 20:10:55 GMT
- Title: Conveying the Predicted Future to Users: A Case Study of Story Plot
Prediction
- Authors: Chieh-Yang Huang, Saniya Naphade, Kavya Laalasa Karanam, Ting-Hao
'Kenneth' Huang
- Abstract summary: We create a system that produces a short description that narrates a predicted plot.
Our goal is to assist writers in crafting a consistent and compelling story arc.
- Score: 14.036772394560238
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Creative writing is hard: Novelists struggle with writer's block daily. While
automatic story generation has advanced recently, it is treated as a "toy task"
for advancing artificial intelligence rather than helping people. In this
paper, we create a system that produces a short description that narrates a
predicted plot using existing story generation approaches. Our goal is to
assist writers in crafting a consistent and compelling story arc. We conducted
experiments on Amazon Mechanical Turk (AMT) to examine the quality of the
generated story plots in terms of consistency and storiability. The results
show that short descriptions produced by our frame-enhanced GPT-2 (FGPT-2) were
rated as the most consistent and storiable among all models; FGPT-2's outputs
even beat some random story snippets written by humans. Next, we conducted a
preliminary user study using a story continuation task where AMT workers were
given access to machine-generated story plots and asked to write a follow-up
story. FGPT-2 could positively affect the writing process, though people favor
other baselines more. Our study shed some light on the possibilities of future
creative writing support systems beyond the scope of completing sentences. Our
code is available at: https://github.com/appleternity/Story-Plot-Generation.
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