[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-293209-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-293209-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","mbti-personality-prediction-for-fictional-characters-using-movie-scripts-paper","MBTI Personality Prediction for Fictional Characters Using Movie Scripts - Paper","","An NLP model that understands stories should also understand the characters inside them. To enable this, Story2Personality is introduced as a benchmark for predicting a movie character’s MBTI or Big 5 personality from the character’s narrative in the script. Experiments show existing text classification models cannot substantially beat random guessing. A multi-view approach leveraging both verbal and non-verbal descriptions improves results and highlights dataset difficulty for narrative comprehension research.",{"@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 & 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task does Story2Personality introduce?","Question",{"text":63,"@type":64},"It predicts a movie character’s MBTI or Big 5 personality types from that character’s narrative texts in movie scripts.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Why is personality prediction from narratives difficult?",{"text":68,"@type":64},"Stories provide diverse narrative clues, long inputs that strain Transformer models, and informative multi-view inputs requiring joint modeling of scene descriptions and dialogues.",{"name":70,"@type":61,"acceptedAnswer":71},"How does the proposed multi-view model improve performance?",{"text":72,"@type":64},"By using both verbal and non-verbal descriptions together, it achieves better results than using only verbal descriptions, improving over baselines by about 2–3%.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},293209,1789660899,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":113,"slug":114},8,30,"research-report",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},"Religion & 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Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":112,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":139,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":80,"read_time":144,"preview_page_count":4},8796095027276,"https://avatar.qwps.com/avatar/d3BzX2FwX3Rlc3RfMjUxMTI2XzAxODA=","MBTI Personality Prediction for Fictional Characters Using Movie  \nScripts  \nYisi Sang 1 􀀃 Xiangyang Mou2􀀃 Mo Yu3􀀃 Dakuo Wang4 Jing Li5 Jeffrey Stanton 1  \n1 Syracuse University 2Rensselaer Polytechnic Institute 3Pattern Recognition Center, WeChat AI  \n4IBM Research, Northeastern University 5New Jersey Institute of Technology  \n[yisang@syr.edu](yisang@syr.edu) [moux4@rpi.edu](moux4@rpi.edu) [moyumyu@tencent.com](moyumyu@tencent.com)  \narXiv :2210 . 10994v 1 [ cs .AI] 20 Oct 2022  \nAbstract  \nAn NLP model that understands stories should  \nbe able to understand the characters in them. To support the development of neural models for this purpose, we construct a benchmark, Story2Personality. 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.  \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.  \nWe propose Story2Personality, a new narrative understanding benchmark to encourage the 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  \nwith MBTI labels of four dimensions. In comparison, the only existing related dataset (Flekova and 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 script to a structured form with the verbal character dialogues and the non-verbal scene descriptions illustrating backgrounds. ","cbCaigKUofqjw9Ij","https://ap.wps.com/l/cbCaigKUofqjw9Ij","pdf",1705274,11,"English","# Introduction\n## Character comprehension background\n## Story2Personality benchmark and challenges\n# Related Work\n## Character-centric narrative understanding\n## Long narrative and persona-related work\n## Fundamental NLP annotation techniques","[{\"question\":\"What task does Story2Personality introduce?\",\"answer\":\"It predicts a movie character’s MBTI or Big 5 personality types from that character’s narrative texts in movie scripts.\"},{\"question\":\"Why is personality prediction from narratives difficult?\",\"answer\":\"Stories provide diverse narrative clues, long inputs that strain Transformer models, and informative multi-view inputs requiring joint modeling of scene descriptions and dialogues.\"},{\"question\":\"How does the proposed multi-view model improve performance?\",\"answer\":\"By using both verbal and non-verbal descriptions together, it achieves better results than using only verbal descriptions, improving over baselines by about 2–3%.\"}]","MBTI Personality Prediction for Fictional Characters Using Movie Scripts - Paper | PDF",28]