Modeling Protagonist Emotions for Emotion-Aware Storytelling (2020)

Abstract: Emotions and their evolution play a central role in creating a captivating story. In this paper, we present the first study on modeling the emotional trajectory of the protagonist in neural storytelling. We design methods that generate stories that adhere to given story titles and desired emotion arcs for the protagonist. Our models include Emotion Supervision (EmoSup) and two Emotion-Reinforced (EmoRL) models. The EmoRL models use special rewards designed to regularize the story generation process through reinforcement learning. Our automatic and manual evaluations demonstrate that these models are significantly better at generating stories that follow the desired emotion arcs compared to baseline methods, without sacrificing story quality.

## introduction stories are an integral part of human culture. they allow us to express emotions, share knowledge, and to shape our perspective of the world @xcite . stories are made interesting through emotions that connect the characters, their motivations, goals, and achievements @xcite . cognitive scientists have pinpointed the central role of emotions in storytelling @xcite . early automatic storytelling systems based on symbolic planning also showed that addressing character emotions for plot construction resulted in more diverse and interesting stories @xcite @xcite . however, these studies were rule-based and limited to small-scale data. the advent of deep learning has shifted computational storytelling efforts towards neural methods @xcite . however, despite the broad recognition of its importance, neural story generation methods have not explored the modeling of emotional trajectory. title (input): raw burger emotion arc (input): @xmath0: tom went to a burger place with his friends. he ordered a burger. when he got it , he noticed that it was raw. tom yelled at the waiter for it being raw. he was really disappointed. figure 1 : an example story generated by our model for a given title and emotion arc of the protagonist. story segments are highlighted with the emotions the protagonist (tom) experiences. in this paper, we present the first study to take into account the emotional trajectory of the protagonist in neural story generation. research in cognitive science has shown that while comprehending narratives, readers closely monitor the protagonist's emotional states @xcite . however, emotions experienced by the protagonist might differ from the general emotions expressed in the story. for example, the general emotion of "my boss was very angry and decided to fire me." is anger, but the narrator's emotional reaction would be to feel upset. at any point in a story, we represent the protagonist's emotions using a set of basic emotions. the theory of basic emotions is well-accepted in psychology, but there is little consensus about the precise number of basic emotions. @xcite proposed 8 primary emotions, and @xcite first proposed 7 and then changed to 6 basic emotions. following recent theories @xcite , we choose anger, fear, joy, and sadness, to describe the protagonist's emotions. we additionally include neutral to account for cases with no strong emotions. we refer to these 5 emotions as basic emotions. moreover, emotions evolve within a narrative. for modeling the evolving emotions of the protagonist, we define an emotion arc for a story. our definition is inspired by prince's change-of-state formalization @xcite , which asserts that stories are about change. according to this theory, a story has three components: a starting state; an ending state; and events that translate the starting into the ending state. motivated by this, we define the emotion arc as a sequence of three basic emotions that describe the starting, body, and ending emotional states of the protagonist. given a story title and the emotion arc of the protagonist as inputs, our goal is to generate a story about the title that adheres to the given emotion arc. fig. 1 shows an example story generated by our model, where the protagonist's emotion evolves from joy to anger and then sadness. to address this problem, we present three models based on gpt-2 @xcite that incorporate the protagonist's emotion arc as a controllable attribute while preserving content quality: an emotion supervision (emosup), and two emotion-reinforced (emorl) models based on reinforcement learning. the emorl models use two emotion-consistency rewards, ec-em and ec-clf. ec-em uses semantically enhanced emotion matching to encourage the model to adhere to the given emotion arc. it infers the protagonist's emotions in the generated stories using commonsense transformers, comet @xcite . ec-clf achieves the same goal using a classifier to infer the protagonist's emotions. in the absence of a training corpus of stories labeled with the protagonist's emotions, we automatically annotate a large-scale story corpus using comet. our automatic and manual evaluations show that our models can not only express the desired emotion arcs but also produce fluent and coherent stories. our contributions are: ## related works early story generation systems relied on symbolic planning @xcite @xcite or case-based reasoning @xcite . although these systems could ensure long-term coherence, they could only operate in predefined domains and required manual engineering. these problems have been somewhat alleviated by recent seq2seq storytelling models @xcite , some of which are based on intermediate representations @xcite @xcite @xcite . recent approaches have also used large-scale language models (lms) based on transformers @xcite , such as gpt-2 @xcite . being trained on large amounts of data, these models can generate highly fluent text and find applications in story generation @xcite and dialogue systems @xcite . however, they lack the ability to dictate any auxiliary objective for the generated text, such as expressing specific attributes. to address this, approaches such as conditional training or weighted decoding have been proposed to control different properties of the generated text such as sentiment, tense, speaker style, and length @xcite @xcite @xcite . @xcite use reinforcement learning for generating goal-driven story plots, which are sequences of event tuples. @xcite propose pplm, which uses an attribute classifier to steer text generation without further training the lm. more closely related to our work is generating automatic responses with a specific sentiment or emotion @xcite @xcite . modeling characters @xcite , @xcite , @xcite , @xcite , @xcite , @xcite inter alia) and sentiment trajectory @xcite , @xcite inter alia) has been shown to be useful for story understanding, in general. however, there are limited works on incorporating characters or sentiment for story generation. previous work model characters but not sentiment @xcite . @xcite is a contemporary and unpublished work that incorporates sentiment while "filling in" a narrative. @xcite and @xcite control the overall sentiment for story ending generation. these works are limited to coarse-grained sentiments and/or only target the ending sentence. instead, we model the emotional trajectory of the protagonist as the story progresses, which is more central to storytelling than the overall sentiment. ## emotion-aware storytelling we first explain how our models can track the protagonist's emotional trajectory ( §3.1). we then define the problem statement ( §3.2) and followed by an introduction to our base storytelling model ( §3.3), which is used as the backbone of our three proposed models ( §3.4 and §3.5) foot_0 . ## results and discussion we first describe our experiments on choosing the base storytelling model ( §5.1) followed by evaluation of the proposed models ( §5.2). ## conclusion in this paper, we proposed the emotion-aware storytelling task for modeling the emotion arc of the protagonist. to this goal, we designed two emotionconsistency rewards using a commonsense transformer and an emotion classifier. experiments demonstrated that our approach improved both content quality and emotion faithfulness of the generated stories. we also presented two case studies, which show interesting use cases of our model. in general, such models can have educational applications by enabling children to explore creative writing at an early age and addressing the literary learning needs of learners with disabilities. this paper is a step towards future research directions on planning emotional trajectory while generating stories. using commonsense inferences about the effect of the events on emotional states of various characters of the story has the potential of generating more coherent, realistic, and engaging stories. in this work, we focused only on the protagonist, but future works can explore modeling motivations, goals, achievements, and emotional trajectory of all characters. our approach is general and provides a blueprint for similar works going forward and can be used outside emotion-aware storytelling, e.g., for generating other emotional content or text with other attributes or properties. the various assumptions and choices made in this paper and the specific characteristics of the dataset we chose can introduce biases and errors. for example, comet is a discourse-agnostic model, and separately extracting emotional reactions for each sentence may fail to maintain emotional consistency with the rest of the narrative. such sources of errors and biases need further systematic investigation.