Evaluating Gender Bias Transfer from Film Data (2022)

Abstract: Films are a rich source of data for natural language processing. OpenSubtitles (Lison and Tiedemann, 2016) is a popular movie script dataset, used for training models for tasks such as machine translation and dialogue generation. However, movies often contain biases that reflect society at the time, and these biases may be introduced during pre-training and influence downstream models. We perform sentiment analysis on template infilling (Kurita et al., 2019) and the Sentence Embedding Association Test (May et al., 2019) to measure how BERT-based language models change after continued pre-training on OpenSubtitles. We consider gender bias as a primary motivating case for this analysis, while also measuring other social biases such as disability. We show that sentiment analysis on template infilling is not an effective measure of bias due to the rarity of disability and gender identifying tokens in the movie dialogue. We extend our analysis to a longitudinal study of bias in film dialogue over the last 110 years and find that continued pre-training on OpenSubtitles encodes additional bias into BERT. We show that BERT learns associations that reflect the biases and representation of each film era, suggesting that additional care must be taken when using historical data.

## introduction movies are often seen as a commentary on or reflection of society. they can reveal key themes within a culture, showcase the viewpoints of various social classes, or even reflect the writer's internal mindset. additionally, movies have widespread influence on audience perceptions based on the messages they contain. movie scripts are popular data sources for training models for natural language tasks, such as sentiment analysis @xcite and dialogue systems @xcite , because they are * equal contribution written to mimic natural human dialogue, easy to collect, and much more cost effective than transcribing human conversations. however, despite this popularity, there has been concern regarding the biases that movies contain @xcite and the potential downstream effects of training on biased datasets @xcite . more specifically, gender bias in movies is a long-studied issue. a popular benchmark for gender representation is the bechdel test foot_0 . a movie passes the bechdel test if it contains two female characters who speak to each other about something other than a man. in the last decade, the bechdel test has come under criticism. @xcite argues that the bechdel test is a poor metric in three ways: it excuses "low, one-dimensional standards" for representation, it fails to consider intersectionality of oppression, and it treats all conversation about men as unempowering. as a more intersectional and nuanced method of measuring bias and stereotyping in movie script datasets, we propose fine-tuning a language model on movie scripts in order to examine bias that the model inherits from movies and its impact on downstream tasks. particularly, a model trained on movie scripts may inherit biases or offensive language from the source material, which can lead to differing treatment of social groups in applications of the model. in a longitudinal analysis of bias over time, we evaluate how models that are finetuned on separate decades of movie scripts reflect societal biases and and historical events at the time. the form of fine-tuning we use is a continuation of the pre-training objectives on the new dataset. the contributions of this paper are: ## bias statement in our analysis we use a language modeling approach to uncover and examine bias in a movie script corpus. our main focus is gender bias, but we will also explore intersectional bias between gender and disability. we define bias as implicit bias that may result in a difference in treatment across two groups, regardless of whether that difference causes harm. this definition of implicit bias follows from the premise of the implicit bias association test @xcite , which demonstrated that implicit biases impact behavior. our analysis also considers both explicit and implicit gender biases that have the capability for harm. in this paper we assume biases in movies are intentional, but it is possible the author may have been using these stereotypes as a method of raising awareness of an issue or as satire. it is important to note that models trained on these movie scripts will likely not be able to pick up on the intent of the author, but rather will learn and amplify the biases @xcite . this analysis includes a comparison between the treatment of men and woman in film scripts, which implicitly upholds a gender binary. we fine-tune bert on full movie scripts without partitioning by gender, but we examine gender bias by comparing the associations the model has learned about men and women during the analysis. by discarding data about people who are nonbinary, we make this analysis tractable, but we also lose the ability to draw meaningful conclusions about this underrepresented group. we choose to reduce harm by not assuming the genders of characters; rather, we consider the associations the model has learned about gender from the speech of all characters. thus, our analysis is more likely to represent biases in how characters discuss men and women who are not present, rather than how characters treat men and women in direct conversation. ## related work a significant amount of research has examined and quantified gender bias in movie scripts and narratives. past work has focused on bias in film dialogue, using classification models to predict whether speakers are both female, both male, or of different genders. @xcite concluded that simpler lexical features are more useful than sentiment or structure when predicting gender. @xcite use gender ladenness, a normative rating representing word association to feminine and masculine traits, to explore gender bias. specifically, they examine gender ladenness with respect to the movie's genre, showing that certain genres are more likely to be associated with masculine/feminine traits than others. @xcite add to the genre and gender association, finding that certain sports, war, and science fiction genres focus on male-dominated tropes and that male-dominated tropes exhibit more topical diversity than female-dominated tropes. @xcite show that in generated stories, male protagonists are portrayed as more intellectual while female protagonists are portrayed as more sexual. @xcite look at more subtle forms of gender bias as it relates to power and agency. their work uses an extended connotation lexicon to expose fine-grained gender bias in films. @xcite also looked at the differences in portrayals of characters based on their language use which includes the psycholinguistic normative measures of emotional and psychological constructs of the character. they found that female writers were more likely to have balanced genders in movie characters and that female characters tended to have more positive valence in language than male counterparts in movie scripts. while these works focus on understanding bias in film directly, we take a slightly differently framing, examining how the bias in a film dataset can impact the biases of a language model. @xcite examine concept drift and generalization on language models trained on twitter data over time. our work on longitudinal effects of film data is distinct in timescale (reflecting the much slower release rate of films relative to tweets) and in motivation; @xcite consider the effects of the data's time period on model performance, while we examine the effects of the time period on model biases. ## methods we examine how a bert-based language model @xcite may inherit bias from film data. specifically, we use the opensubtitles corpus @xcite , a collection of movie subtitles from approximately 400,000 movies. while the corpus does not provide summary statistics, upon inspection it appears the vast majority of these movies are american-produced films. these subtitles do not contain speaker gender, and often do not provide speaker names. thus, any bias exhibited in the model is likely from the way the characters speak about people from different groups-e.g. indirect, not direct, sexism. we use the opensubtitles corpus to gather sentences within each movie script and randomly mask words to fine-tune bert on the movie corpora. following previous work by von @xcite that focused on toxic language detection in bert fine-tuned on movie corpora, we considered bias in the original english pre-trained bert as a baseline and bert fine-tuned on movie corpora (which we call filmbert) as a secondary model. we used two approaches to quantify bias in the models, which we describe in the following sections. we then employ a longitudinal analysis of bert by fine-tuning on decades from 1910 to 2010 in order to quantify what societal trends and biases the model may absorb. ## fine-tuning on entire corpora results first, we consider results from continued pretraining over the entire opensubtitles dataset. ## longitudinal study results we use the sentence embedding association test @xcite to quantify the bias in each of the decade models, using the original association tests designed by the authors. these tests measure the association between two contrasting sets of identity terms (e.g. male-identifying and femaleidentifying terms) and two non-identity-based sets (e.g. career-related terms and family-related terms). we consider only associations that are significant (@xmath0.05), and factor both the number of significant associations found and the relative effect sizes into our analysis. ## conclusions we find that continued pre-training on film dialogue can encode additional biases and social themes into bert. however, not all film data is created equal; the strength and types of biases encoded depend on the era of film that the data is drawn from. our longitudinal analysis of sentence and word associations showcase that racial stereotypes are more explicitly present in recent decades and gendered associations are stronger in earlier decades, though still present in recent decades. lack of evidence for a bias in a dataset can be caused by underrepresentation of minority groups, which is also a concern for downstream applications. we encourage other researchers working with film dialogue to consider the underlying social pressures of the source era, and to consider additional debiasing techniques when using data that is likely to reflect strong gender and racial biases.