CHAE : Fine-Grained Controllable Story Generation with Characters, Actions and Emotions (2022)

Abstract: 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 .

## introduction story generation, one of emergent tasks in the field of natural language generation, requires following sentences given the beginning of the story. for human beings, it is believed that storytelling requires strong logical thinking ability and organizational competence, and for machines it may be even more intractable. nonetheless, works on story generation can help machines communicate with humans and drive improvements in natural language processing @xcite . at present, most works on story generation focus on the coherence of the story generated according to keywords, outlines and commonsense knowledge @xcite @xcite @xcite . some other works aim at generating stories controlled by overall emotion, style, and topic * corresponding author @xcite @xcite . however, in reality, people often expect more detailed designs catering to their needs rather than a simple theme or topic in the generated story. for example, a novel with more complete elements, i.e., plot, character, theme, viewpoint, symbol, and setting is usually preferred to those made up out of thin air. taking the control in story generation as the cutting point, gpt-2 @xcite can fulfill the story according to the beginning, but the process of generation cannot be controlled by people, resulting in unlogical outputs that lack practicality. ctrl @xcite can specify the generation of articles with different styles through some style words, but such control stays at the coarse-grained level, and makes a relatively weak influence. cocon @xcite introduces natural language to guide text generation. @xcite propose a new task that guides paragraph generation through a given sequence of outline events. however, the above two studies just explicitly add some contents to the generated sentences, which is similar to forming sentences with given phrases, not using the input as a condition guide for the generative models. socp presented by @xcite can generate stories under changeable psychological state control, while it does not govern the detailed contents of the story. in this paper, we consider more fine-grained control on story generation, and propose a model, chae for fine-grained controllable story generation, allowing the generation of stories with customized characters, and their actions and emotions. characters are the core of the story. their actions drive the story along, and their emotions make the story lively and interesting. consequently, we take the characters along with their actions and emotions as the control conditions. it is a challenge that our model needs to control multiple characters with their actions and emotions respec-tively in a story, especially under the guidance in the form of natural language. to crack the nut, a novel input form conducive to fine-grained control on story generation is introduced into chae. concretely, we use various prompts for fine-grained control conditions in different aspects. moreover, we design different methods for different control conditions to improve the control effect. inspired by multi-task learning, we incorporate a characterwise emotion loss while training, thus enforcing the relevance between the characters and their emotions respectively. the contributions of our work can be summarized as follows: ## related work story generation story generation has attracted more and more researchers to explore in recent years. there are many challenges in the task, such as context coherence and control. for context coherence, some works are devoted to introducing a series of keywords @xcite , outlines @xcite , or incorporating external knowledge @xcite @xcite into the story. for style and sentiment control, kong et al. (2021) generate stories with specified style given a leading context. however, it only focuses on the global attributes of the story. brahman and chaturvedi (2020) work on generating stories with desired titles and the protagonists' emotion arcs, and xu et al. (2020) generate stories considering the changes in the psychological state, while they just control the emotion lines instead of the detailed contents. controllable text generation we have witnessed the great performance of sota models for text generation these years. despite the progress in coherence and rationality of the text generated, controllability remains to be challenging, which means generating text with specific attributes, such as emotion, style, topic, format, etc. ctrl @xcite can control the overall attributes such as domain, style and topic of the generated text by adding control codes and prompts. by plugging in a discriminator, pplm @xcite can guide text generation without further training the language model. cocon @xcite finetunes an intermediate block with self-supervised learning to control high-level attributes i.e., sentiment and topic. compared to the previous works, our work places the emphasis on more fine-grained, all-round control on the generating process, including the control of characters with their emotions and actions in the story. ## conclusion and future work through our model chae, we can create stories with fine-grained control according to the specified characters and corresponding actions and emotions, which is more convenient for practical applications, such as the creation of script novels, providing inspiration for screenwriters, and even acting as screenwriters in the future. however, our model also has some disadvantages: (1) the dataset contains only stories with 5 sentences, which is not enough for learning to generate longer stories. (2) the training of our model heavily relies on the annotations of characters, emotions, and actions in the dataset, while it is very expensive to obtain the annotated data. (3) the chae in the dataset actually has some noise, i.e., some descriptions in chae are not reflected in the corresponding sente nces. (4) our iterative generation method will result in a long time to train a story and may cause a cascade problem, which affects the overall quality of story generation. in future work, we will adopt datasets with much more data and longer stories, such as writing-prompts @xcite and wikiplots foot_1 . in addition, we will consider using commonsense reasoning techniques to reason about the emotions and actions of the characters in the story before further generating the story. regarding the noise of chae, we plan to conduct denoising in preprocessing to filter out the samples whose chae are inconsistent with the corresponding sentence. the problem can also be alleviated by dynamically controlling the weight of the conditions. we are also further exploring more convenient and effective training methods to generate controllable stories by inputting control conditions in one go, rather than iterative generation.