Towards Inter-character Relationship-driven Story Generation (2022)

Abstract: In this paper, we introduce the task of modeling interpersonal relationships for story generation. For addressing this task, we propose Relationships as Latent Variables for Story Generation, (ReLiSt). ReLiSt generates stories sentence by sentence and has two major components - a relationship selector and a story continuer. The relationship selector specifies a latent variable to pick the relationship to exhibit in the next sentence and the story continuer generates the next sentence while expressing the selected relationship in a coherent way. Our automatic and human evaluations demonstrate that ReLiSt is able to generate stories with relationships that are more faithful to desired relationships while maintaining the content quality. The relationship assignments to sentences during inference brings interpretability to ReLiSt.

## introduction interpersonal relationships between characters are, in many ways, the glue that holds a story together. almost every story revolves around at least one, if not more, inter-character relationships. despite the importance of relationships in stories @xcite , only few studies in nlp have explored story generation from the perspective of character relationships. recent story generation methods typically generate stories from a prompt @xcite or a planner detailing events or keywords of the story @xcite @xcite . while these methods can generate stories based on open-ended prompts and plans they can neither encode character relationships nor can they give explicit control over the characters and their relationships. in this paper, we introduce relationship-driven story generation where given a prompt sentence and a set of inter-character relationships, the goal is to generate a story following the prompt sentence which exhibits the desired the story starts with amy going to the market. it starts to rain and amy had forgotten her umbrella. amy's friend julia lends amy her umbrella. amy is approached by wisk who pressures her for money [...] ## related work story generation was first approached with symbolic planning @xcite @xcite . since then, neural networks have gained more interest for story generation. several of these methods generate stories using coarse-grained prompts or outlines @xcite @xcite @xcite or event-based plans @xcite @xcite besides the development of plan through keywords or events, there are other elements that contribute to a good story. characters @xcite @xcite @xcite , their sentiment trajectory @xcite and relationships with others @xcite @xcite have been shown to be useful for story understanding, in general. for example, @xcite proposed using interpersonal relationships for predicting the best story continuation in first person dialogue-based stories. characters have also been shown to be useful for language generation such as in dialogue systems for generating responses conditioned on character's persona @xcite @xcite . nevertheless, only a few works have modeled characters for story generation. existing character-centric storytelling systems have conditioned generation on automatically learnt character embeddings @xcite or persona @xcite . we instead model characters through their interpersonal relationships. our work is also related to the problem of text generation conditioned on sentiment or emotions. @xcite and @xcite proposed controlling the sentiment for story ending generation. @xcite proposed incorporating sentiment for the story in-filling task. @xcite generate stories that adhere to desired emotional arcs for the protagonist. in story generation, jhamtani and berg-kirkpatrick (2020) use latent "anchor words" in each sentence as a plan that generates the story. @xcite employ variational autoencoders to generate stories with informative latent variables for more diverse and coherent story generation. relist provides control over relationships in story generation by considering relationship expressed in a sentence as a latent variable. relist and its training procedure (sec. 3.2). ## experimental setup in this section we explain the dataset used for training and initialization of relist (sec. 4.1) followed by baselines (sec. 4.2) and evaluation measures (sec. 4.3). ## experimental results here, we first report the automatic and human evaluation results, followed by ablations and analyses of relist. ## conclusion this paper presents a new perspective in story generation-from relationships. the proposed approach, relist, introduces relationships as latent variables for relationship-driven story generation. we jointly train the components of relist while addressing the major challenges of this task. relist outperforms baselines in generating coherent stories with desired relationships. finally, we also observe how the latent variable based design of relist offers interpretability to the generation process without compromising its performance.