MBTI Personality Prediction for Fictional Characters Using Movie Scripts (2022)

Abstract: An NLP model that understands stories should be 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.

## introduction character comprehension is commonly regarded as the cornerstone to comprehending stories in psychology and education @xcite @xcite . 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) @xcite . moreover, most of them are limited in factoid understanding of characters, such as coreference resolution @xcite and character relationships @xcite , and few studies have explored deeper comprehension of characters' persona @xcite , on which humans can generally do well. we propose story2personality, a new narrative understanding benchmark to encourage * authors contributed equally to this paper. mo yu is the corresponding author. 1 our code and data are released at https://github. com/yisisang/story2personality the study of character understanding. the goal of story2personality is to predict personality according to the character's narrative texts in the script. personality 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) @xcite . second, the inputs of the task are long (>10k words on average), challenging the applications of transformer-based models @xcite . third, both the scene descriptions and dialogues are informative for the prediction, requiring models to jointly consider multiple views of inputs. this study makes the following contributions: • 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 @xcite 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. ## story2personality dataset we constructed our dataset in three stages: extracting movie scripts from the internet movie script database (imsdb foot_0 ), parsing the collected movie scripts into dialogue and scene sections, matching characters' personality types from the personality database(pdb foot_1 ) with their dialogues and scenes. ## dataset analysis we conduct human study to verify the advantage of our script parser; then provide the human performance on our dataset. ## experiments baselines we build two baseline models. our method we propose the multi-view multirow bert (mv-mr bert) classifier (fig. 2 ) which is an extension of bert to deal with the long inputs and handle the verbal and non-verbal information differently. first, to handle the long input per character, we borrow the idea from fusionin-decoder @xcite . since the complexity of transformers is o(rl 2 ) (with r the number of rows and l the length per row), when l is very large, we can split it into multiple segments to reduce the quadratic term. next, we rely on the attention over all the segments to fuse the information. specifically, we split the input content d of a character into multiple segments @xmath0, and encode all the segments in a minibatch as @xmath1, where d is the hidden state size. then a linear head is applied to get the attention score across tokens in all the rows as α = softmax(hw + b) ∈ [0, 1] r×l . the final summarized representation of the input d is thus the second, to handle both the dialogue and behavioral description a character, our multi-view model receives an input pair (d dial , d scene ), then uses a shared bert and separated linear heads to compute the summarized states h dial d and h scene d . the two vectors are fed into a fully-connected layer for prediction. for the scene descriptions, we prepend a special token "[ent]" to the target character's name to denote its position. the attention α scene is only computed on these special tokens. ## limitation movie scripts are the blueprint for the actor's performance. an actor's dialogue, body language, and the contexts are well described in the scripts. there is sufficient information in the scripts for readers to understand the characters. however, the actors' portrayal have the potential to add additional cues to influence the audience's perception of the fictional characters' personalities. in future work we will try to use multi-modality data as input. ## conclusion we develop a movie script parser and proposed a new narrative understanding benchmark, story2personality, which enables neural model training for understanding characters. we evaluate several classifiers on our task -while our multi-view multi-view bert model achieves a substantial improvement over the svm and bert baselines, there is a huge gap compared to human performance. this indicates our dataset a valuable and challenging task for future research. in the future we will expand our dataset.