Learning Similarity between Movie Characters and Its Potential Implications on Understanding Human Experiences (2021)

Abstract: While many different aspects of human experiences have been studied by the NLP community, none has captured its full richness. We propose a new task to capture this richness based on an unlikely setting: movie characters. We sought to capture theme-level similarities between movie characters that were community-curated into 20,000 themes. By introducing a two-step approach that balances performance and efficiency, we managed to achieve 9-27% improvement over recent paragraph-embedding based methods. Finally, we demonstrate how the thematic information learnt from movie characters can potentially be used to understand themes in the experience of people, as indicated on Reddit posts.

## introduction what makes a person similar to another? while there is no definitive answer, some aspects that have been investigated in the nlp community are personality @xcite , demographics @xcite as well as personal beliefs and intents @xcite . while each of these aspects is valuable on its own, they also seem somewhat lacking to sketch a complete picture of a person. researchers who recognise such limitations seek to ameliorate them by jointly modelling multiple aspects at the same time @xcite . yet, we intuitively know that as humans, we are more than the sum of the multiple aspects that constitutes our individuality. our human experiences are marked by so many different aspects that interact in ways that we can not anticipate. what then can we do to better capture the degree of similarity between different people? finding similar movie characters can be an interesting first step to understanding humans better. 1 code and data available at https://github.com/ zhilin123/similar_movie_characters many characters are inspired by and related to true stories of people so understanding how to identify similarities between character descriptions might ultimately help us to better understand similarities in human characteristics and experiences. one way of defining what makes movie character descriptions similar is when community-based contributors on all the tropes foot_0 classify them into the same theme (also known as a trope), with an example from the trope "driven by envy" shown in other themes (tropes) include "parental neglect", "fallen hero", and "a friend in need". such community-based curation allows all the tropes to reap the same advantages as wikipedia and open-sourced software: a large catalog can be created with high internal-consistency given the in-built self-correction mechanisms. this approach allowed us to collect a dataset @xmath0,000 themes without requiring any annotation cost. based on this dataset, we propose a model that can be used to identify similar movie characters precisely yet efficiently. while movie characters may not be the perfect reflection of human experience, we ultimately show that they are good enough proxies when collecting a dataset of similar scale with real people would be extremely expensive. our key contributions are as follows: superman's 1990s enemy conduit. conduit hates superman because he knows if superman wasn't around he would be humanity's greatest hero instead ... ## task formulation there is a set of unique character descriptions from the all the tropes (character 0 , character 1 ... character n ), each associated with a non-unique trope (theme) (t rope 0 , t rope 0 ... t rope p ). given this set, find the k (where @xmath1, 5 or 10) most similar character(s) to each character without making explicit use of the trope association of each character. in doing so, the goal is to have a maximal proportion of most similar character(s) which share the same tropes. ## methods in this section, we first discuss how we prepare the dataset and trained a bert next sentence prediction (nsp) model to identify similar characters. based on this model, we present a 2-step select and refine approach, which can be utilized to find the most similar characters quickly yet effectively. ## evaluation in this section, we first present evaluation metrics and then present the preparation of baseline models including state-of-the-art paragraph-level embedding models. finally, we analyze the performance of our models relative to baseline models. ## conclusion we introduce a pioneering study on identifying similar movie characters through weakly supervised learning. based on this task, we introduce a novel select-and-refine approach that allows us to match characters belonging to a common theme, which simultaneously optimize for efficiency and performance. using this trained model, we demonstrate the potential applications of this study in identifying movie characters that are similar to human experiences as presented in reddit posts, without any fine-tuning. this represents an early step into understanding the complexity and richness of our human experience, which is not only interesting in itself but can also complement research in social media analysis, personalizing dialogue agents and media recommendations/interactions.