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A multi-view approach that leverages both verbal dialogue and non-verbal scene descriptions improves results over verbal-only models, motivating narrative comprehension for character persona.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/mbti-personality-prediction-for-fictional-characters-using-movie-scripts-research/269472/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/mbti-personality-prediction-for-fictional-characters-using-movie-scripts-research/269472.png","ImageObject",300,407,{"name":42,"@type":43},"Cart","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-24","2026-09-15",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",10,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What is the Story2Personality task?","Question",{"text":63,"@type":64},"Story2Personality predicts a movie character’s personality (MBTI or Big 5) using the character’s narrative text in the movie script. The dataset focuses on understanding persona expressed through narrative clues.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Why is the task difficult for existing text classification models?",{"text":68,"@type":64},"The inputs are long and include multiple narrative views such as scene descriptions and dialogues, which require joint reasoning. Experiments show baselines cannot largely outperform random guesses.",{"name":70,"@type":61,"acceptedAnswer":71},"How does the proposed multi-view model improve personality prediction?",{"text":72,"@type":64},"The model uses both verbal and non-verbal descriptions rather than only dialogue text. 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The task is to predict a movie character’s MBTI or Big 5 personality types based on the narratives of the character. Experiments show that our task is challenging for the existing text classification models, as none is able to largely outperform random guesses. We further proposed a multi-view model for personality prediction using both verbal and non-verbal descriptions, which gives improvement compared to using only verbal descriptions. The uniqueness and challenges in our dataset call for the development of narrative comprehension techniques from the perspective of understanding characters.1  \n1 Introduction  \nCharacter comprehension is commonly regarded as the cornerstone to comprehending stories in psychology and education (Bower and Morrow, 1990 ; Paris and Paris, 2003 ; Zhao et al., 2022) . The NLP community has done some work on character comprehension in reading comprehension tasks, but most of the existing studies focus on short or expository texts (e.g., story summaries) (Urbanek et al., 2019 ; Brahman et al., 2021) . Moreover, most of them are limited in factoid understanding of characters, such as coreference resolution (Chen and Choi, 2016) and character relationships (Iyyer et al., 2016), and few studies have explored deeper comprehension of characters’ persona (Flekova and Gurevych, 2015 ; Sang et al., 2022a), on which humans can generally do well.  \nWe propose Story2Personality, a new narrative understanding benchmark to encourage  \n∗Authors contributed equally to this paper. Mo Yu is the corresponding author.  \n1Our code and data are released at [https://github](https://github) . com/YisiSang/Story2Personality  \nFigure 1: An example excerpt from the movie script of “The Matrix”. Blue utterances are the character Morpheus’s scene descriptions, red are his dialogues. Morpheus’s MBTI personality was rated as ENFJ by 300 user votes.  \nthe study of character understanding. The goal of Story2Personality is to predict personality according to the character’s narrative texts in the script.  \nPersonality prediction from narratives has many challenges. First, stories often use a variety of narrative clues (e.g., scenery changes), sequence (e.g., flashback) and rhetorical techniques (e.g., metaphor) (Xu et al., 2022b) . Second, the inputs of the task are long (>10K words on average), challenging the applications of Transformer-based models (Vaswani et al., 2017) . Third, both the scene descriptions and dialogues are informative for the prediction, requiring models to jointly consider multiple views of inputs.  \nThis study makes the following contributions:  \n• We establish a large-scale dataset for personality prediction of narrative characters that can support the development of neural models. Our dataset consists of 3,543 characters from 507 movies with MBTI labels of four dimensions. In comparison, the only existing related dataset (Flekova  \n6715  \nFindings of the Association for Computational Linguistics: EMNLP 2022 , pages 6715–6724 December 7-11, 2022 ©2022 Association for Computational Linguistics  \nand Gurevych, 2015) contains only 298 characters and focuses on a single dimension. Our dataset is proved challenging—on this binary classification task, none of the baselines achieve higher than 60% macro-F1 .  \n• We develop a movie script parser to automatically process a scrip","cbCaipYandcJeiON","https://ap.wps.com/l/cbCaipYandcJeiON","pdf",1678816,"English","# Introduction\n# Related Work\n## Character-Centric Narrative Understanding","[{\"question\":\"What is the Story2Personality task?\",\"answer\":\"Story2Personality predicts a movie character’s personality (MBTI or Big 5) using the character’s narrative text in the movie script. The dataset focuses on understanding persona expressed through narrative clues.\"},{\"question\":\"Why is the task difficult for existing text classification models?\",\"answer\":\"The inputs are long and include multiple narrative views such as scene descriptions and dialogues, which require joint reasoning. Experiments show baselines cannot largely outperform random guesses.\"},{\"question\":\"How does the proposed multi-view model improve personality prediction?\",\"answer\":\"The model uses both verbal and non-verbal descriptions rather than only dialogue text. This multi-view design yields an improvement compared with using verbal descriptions alone.\"}]","MBTI Personality Prediction for Fictional Characters Using Movie Scripts - Research | PDF",25]