Psychology-guided Controllable Story Generation (2022)

Abstract: Controllable story generation is a challenging task in the field of NLP, which has attracted increasing research interest in recent years. However, most existing works generate a whole story conditioned on the appointed keywords or emotions, ignoring the psychological changes of the protagonist. Inspired by psychology theories, we introduce global psychological state chains, which include the needs and emotions of the protagonists, to help a story generation system create more controllable and well-planned stories. In this paper, we propose a Psychology-guided Controllable Story Generation System (PICS) to generate stories that adhere to the given leading context and desired psychological state chains for the protagonist. Specifically, psychological state trackers are employed to memorize the protagonist’s local psychological states to capture their inner temporal relationships. In addition, psychological state planners are adopted to gain the protagonist’s global psychological states for story planning. Eventually, a psychology controller is designed to integrate the local and global psychological states into the story context representation for composing psychology-guided stories. Automatic and manual evaluations demonstrate that PICS outperforms baselines, and each part of PICS shows effectiveness for writing stories with more consistent psychological changes.

## introduction controllable story generation (csg) is an important task in natural language processing (nlp) @xcite @xcite . it has also become one of the test methods for progress in artificial intelligence (ai). most existing state-of-the-art works (kong et al., 2021; rashkin et al., 2020; paul and † corresponding author. ‡ in this work, we define the protagonist as the most frequently occurring character in a story @xcite . mike complained to the server, then tory comforted him. the server apologized with a gift and mike replied with a smile. the service was slow and mike began to feel uncomfortable. ## related work early story generation systems relied on symbolic planning @xcite @xcite , which had domain restriction and massive cost of feature engineering. recent seq2seq storytelling models @xcite had partially alleviated these problems, most of which focused on learning better representation for a story @xcite @xcite @xcite . to introduce semantic knowledge into story generation, many methods also employed large-scale pre-trained language models (lm) based on transformer @xcite , like gpt-2 @xcite and bart @xcite . after in-domain training, these models can generate fluent and coherent text, which can be used in story generation @xcite @xcite and dialogue systems @xcite . however, they lacked the ability of controllable generation, such as expressing specific goals. further, aiming at controllable story generation, works had been introduced to control different attributes of the generated text, such as keyword @xcite , style @xcite and length @xcite . for example, @xcite introduced reinforcement learning to generate a goal-driven storyline, which is a sequence of event tuples. pplm @xcite used attribute classifiers to guide text generation without further training of lm. plotma-chines @xcite transformed an outline into a coherent story by tracking the dynamic plot states. @xcite first planned the stylized keywords and then generated the whole story with the guidance of the keywords. and many works considered commonsense knowledge as an attribute for csg. ammanabrolu et al. 2021 performed story generation using soft causal relations, which automatically extracted from existing natural language plot summaries. paul and frank 2021 used the contextualized commonsense inference rules generated by comet @xcite based model to produce a coherent story ending. most related to this work, many methods generated text with a specific sentiment or emotion @xcite @xcite . @xcite present an annotation framework specifically designed to examine the mental states of characters in commonsense based stories. there are some limitations to incorporating sentiment, emotion or psychological state for story generation. previous work modeled characters but not sentiment @xcite . @xcite controlled the overall sentiment for story ending generation. @xcite incorporated sentiment trajectory by a new task that "filling in" a story. brahman and chaturvedi 2020 modeled the emotional trajectory of the protagonist for story generation. xu et al. 2020a generated a story with multiple emotional changes of protagonists based on the given characters and the corresponding psychological state lines. these works are limited to the guiding of emotion scores or tokens or/and target the ending sentence. lately, xie et al. 2022a modeling the relationship among motivations, actions and emotions based on human activities (i.e. story events), which lacks consideration of the global changes in a story. different from the above methods, we respectively model the local and global psychological state changes of the protagonist as the story progresses, which is more central to storytelling than the emotion trajectory. ## task definition we formulate our psychology-oriented controllable story generation task in the following. note that, the length of the whole story is 5 in this paper, and the output story event is in the m-th time point. input the context @xmath0, x 2 , . . . , x m-1 ) to the current story event with m -1 events, where the i-th event x @xmath1, x 2 i , . . . , x k i ) consists of k words. the name of protagonist @xmath2: an example of our task. output y @xmath3, y 2 , . . . , y r ) (also x m in the next time step) stands for the current story event that consists of r words, based on the protagonist's name p, the need history n, the emotion history e, the need chain a n and the emotion chain a e , where y i is the i-th word. ## methodology the overall architecture of our proposed pics system is illustrated in figure 3 . in the following, we will describe each component in more detail. ## conclusion and future work in this paper, we propose a pics system to generate controllable stories that adhere to the story context and protagonist's psychological state chains. specifically, we model and integrate local and global psychological states of the protagonist as the story progress. experiments demonstrate that pics significantly outperforms baselines and each part shows effectiveness. in future work, it is important to build a large-scale dataset for developing psychology-guided controllable story generation, regarding aspects of multilingual and long text. besides, our methodology can be generalized to a wide range of areas, such as automatic storytelling systems and intelligent education agents.