CHAE: Fine-Grained Controllable Story Generation with Characters, Actions and Emotions
This addresses the need for more detailed and customizable story generation in NLP, though it is incremental by focusing on fine-grained aspects rather than a new paradigm.
The paper tackles the problem of generating stories with fine-grained control over characters, actions, and emotions, achieving strong controllability as shown by automatic and human evaluations.
Story generation has emerged as an interesting yet challenging NLP task in recent years. Some existing studies aim at generating fluent and coherent stories from keywords and outlines; while others attempt to control the global features of the story, such as emotion, style and topic. However, these works focus on coarse-grained control on the story, neglecting control on the details of the story, which is also crucial for the task. To fill the gap, this paper proposes a model for fine-grained control on the story, which allows the generation of customized stories with characters, corresponding actions and emotions arbitrarily assigned. Extensive experimental results on both automatic and human manual evaluations show the superiority of our method. It has strong controllability to generate stories according to the fine-grained personalized guidance, unveiling the effectiveness of our methodology. Our code is available at https://github.com/victorup/CHAE.