Television is often seen as a site for subcultural identification and subversive fantasy, including in queer cultures. How might we measure subversion, or the degree to which the depiction of social relationship between a dyad (e.g. two characters who are colleagues) deviates from its typical representation on TV? To explore this question, we introduce the task of stereotypic relationship extraction. Built on cognitive stylistics, linguistic anthropology, and dialogue relation extraction, in this paper, we attempt to model the cognitive process of stereotyping TV characters in dialogic interactions: given a dyad, we want to predict: what social relationship do the speakers exhibit through their words? Subversion is then characterized by the discrepancy between the distribution of the model's predictions and the ground truth labels. To demonstrate the usefulness of this task and gesture at a methodological intervention, we enclose four case studies to characterize the representation of queer relationaBliigtiBeasnign the Theory, Frasier, andGilmore Girls as we explore the suspicious and reparative modes of reading with our computational methods.
Television, often featuring hyper-realized characters, is an important venue for understanding
social and relational identities. Take this scene frBoigmBtahneg Theory:
howard. So, who wants to reFnitddler?
sheldon. No need! We have the special edition.
leonard. Well, maybe weare like Haroun and Tanweer.
(season 1, episode 8)
Haroun and Tanweer are, as just revealed to the characters, a gay couple who recently adopted
a baby. Knowing that they are a gay couple, Leonard immediately assumes that they love the
musical theater thFiadtdler on the Roof represents. Indeed, in popular culture, musical theater
might have been thqeueerest genre. However, in his appraisal of its cultural significance in
queer culture, John M. Clum, writing in 1999, opens with a rather curious note: “The surfeit of
television situation comedy has pretty much killed stage comedy. When you can get crypto-gay
Frasier free every week, who needs the gag-rich musical comed8y,p?”. 1[
Perhaps Clum is right. In that same year, his “crypto-gay” show aired an episode rather reminiscent of Wildean comedy of manners, in which the Crane brothers try to organize a dinner party (or according to them, an “intime soiree”), constantly on the phone inviting their friends, only to find this in the voicemail: allison. We just got invited to a dinner party at Dr. Crane’s. harry. Which Dr. Crane? allison. Does it matter? You get the one, you get that other one. Personally, I think the whole arrangement’s a …little What Alison truly thinks of them is never revealed to the audience, although the quick-witted Frasier jumps to his conclusion: niles. What you suppose she meant by that? frasier. She thinks we’re always together—that we’re some s…orctouopfle.
niles. That’s ridiculous! We spend lots of time apart. Besides, who is she to talk? Look at her and Harry! They go everywhere together.
frasier. They’re married, Niles! Still, there’s no reason for her to coadldl. us (season 6, episode 17) What ensues is another similarly frivolous argument on “who’s the other one,” then another on who not to invite to dinner. Much of this episode is Frasier and Niles arguing in the apartment like an old, married couple (“It’s possible we have grown a tad dependent on one another.”) while appreciating each other ( “So we spend a lot of time together—so what? I enjoy it!”).
Those aforementioned scenes from tBhigeBang Theory andFrasier deal with a key aspect of discursive interactions that constitute the subject of the present study: how certain forms of dialogic interaction index certain social and relational identities, including that of a stereotypical gay man or a married couple. If Frasier and Niloedsdaarse brothers, who are they to each other? If we see their speeches as indexical signifiers, what social identities do they anchor in the interactional context? Built on cognitive s1t0y]laisntdicsso[cial semiotic4s0[], this work seeks to understand how the depiction of a social relationship between a pair of characters (ordayad) in conversation deviates from the typical representation of that relationship type on TV. We hope to advance an operationalization of subversion grounded in queer studies to interrogate the representation of queer relationalities on TV, and enclose four case studies to demonstrate how it can enable more close readings, which has implications for both computational humanities and queer stu1dies.
Our main computational task involves predicting a stereotypical relationship type, given a
dialogue between a dyad (or, two characters) from a scene in a TV series. We follow convention
of relation extraction in N4L5P,1[
comprised of multiple dialogue turns, each an utt e ra=nc(e , 0, … , ), where ∈ denotes the character (or speaker) identity among all ch arinacttheersscen e , and the words they speak, we seek to train a m (o dℎel, ), where ℎ is the character that occupies the head positio n,the corresponding tail, to predict a relationship∈lℛabe,l
In order to carry out this inquiry, we need to identtirfuyetrheelationship for a set of character pairs attended with dialogue in scripts, and build a model of their stereotypical interaction. To support this task, we put together a dedicated dataset of the following components: Dialogues from parsed teleplays.
TV Writing to support this t2asPkil.ot episodes in the teleplay format are preferable for this task because, unlike sampling a random episode from a collection of transcripts, they typically do not require their audience to have any background knowledge of the series itself, and the standardized format of the teleplay gives us reliable scene segmentation as well as other structural information of the interaction (e.g. speaker labels and background action statements). However, digitized PDF files are unstructured. To address this, we leverage the fact that in teleplays, structural elements are distinguished by the amount of indentation a line has. For example, speaker labels are most heavily indented. We used Teseract to perform OCR on the PDF files, 3 which gives us both the recognized texts and the bounding box associated with them for each page. With this information, we used OpenAI’s GPT-4o to parse those OCR’d development, and test sets.
Relationship type labels. Another component is the relationship type labels that we can use for training models and analysis. We first use Wikipedia On-Demand API to identify and gather pages related to each title for which we have the teleplay of the p4iloWtietphisode. this resource, we devise a pipeline of three stages: Durimngintinhgestage, we employ GPT-4o to extract relationship tuples from these summaries without using any predefined relationship labels. This extraction process was followed by a manual review, where we pruned the labels by merging semantically similar ones, suchkiads_“of” and “child_of”; this is thepruning stage. To ensure quality, a co-author replicated the relationship extraction task on a test set comprising 50 titles in our test set. For relations that GPT-4o did extract, the accuracy is satisfactory a8t1.67%. Finally, we verify the accuracy of all extractions in both the development and test sets, which concludes vtehriefication stage. In principle, several of the relationship types can be overlapping—two characters may be seen to be both msaproruiseed_o(f) and lovers l(ove_interest_of). In order to create a multiclass classification problem (predicting only one label for each dyad), we rank all relationship types based on their specificity (as listed in Table3 in AppendixB) and ask a model to predict the mspoesctific relationship type from this set. The most frequent relationship typescoalrlee:ague_of, friend_of, sibling_of, spouse_of, andclassmate_of.
Dyads. To generate dyads for training and analysis, we begin by collecting the set of speaker labels present in each teleplay. These labels are often noisy due to OCR errors and inconsistencies with how names appear in Wikipedia summaries. To standardize these labels, we query the title in the Movie Database (TM5Dwbh),ich provides a list of characters, both recurring and guest. We then match each speaker label with a canonical character name from TMDb using a simple heuristic: if there is at least one token overlap, we select the TMDb character name with minimal edit distance. Labels that do not match (typically generic names like “MAN #1”) are discarded. With standardized speaker labels, we create the list of dyads of interest for each scene by permuting the labels in pairs (i.e. creating 2-permutations from the set of distinct speakers in a scene). This means that a pair of characters is considered only if both have speaking roles within the same scene. Finally, we include a dyad in our dataset if we have previously extracted its relationship type from Wikipedia. See ApCpefonrdisxtatistics and more information.
Anonymization. Previous work indicates speaker anonymization is beneficial as it mitigates the distraction and noise named entities might intr7o].duWce [similarly anonymize our dataset to evaluate its impact. For each scene, we maintain a mapping table between canonical speaker names and randomly assigned entity IDs. All speaker identities are then replaced withENTITY plus their unique scene ID, and their mentions in dialogue lines are anonymized in the same way. Some canonical names include generic words, often related to a profession (e.g. “coach”), and those words would be anonymized, which might lead to an unnecessary 4https://enterprise.wikimedia.com/prod.ucts/ 5https://www.themoviedb.o.rg/ <background> Briefcase in hand , ENTITY 5 is once again waiting for the elevator . He 's approached by ENTITY 6 , 39 , smart , cute , but not sweet . You do n 't get to be hospital administrator and dean of medicine by being sweet . <speaker> ENTITY 6 <line> I was expecting you in my office twenty minutes ago . <speaker> ENTITY 5 <line> Really ? That 's odd because I had no intention of being in your office twenty minutes ago .
Given this data, we can build and evaluate modsetlerseotfyping readers, who learn from the totality of relationships encoded in our training data to infer the relationship enacted between two characters in a specific scene. We do not expect a model to be able to identtruifey the relationship with perfect accuracy—not every relationship is enacted in dialogue at the level of a single scene; but more importantly, our work is premised on the idea that the relationship we observe on screen (and that a model observes as well) can exhibit variation that is deliberately at odds with that “truth.” And yet, accuracy at predicting that truth provides us with an instrumental means to select between diferent models, and we assess several variants in order to find the one most sensitive to the dialogic indicators of how relationships are performed.
ℎ and in scene : supervised andprompting: We compare the performance of the following models on this task to select the best strategy to model stereotyping readers with our data. We first establmisahjotrhitey class (predicting ( the most frequent clacsos,lleague_of) baseline for all models. Since the task takes the form ℎ , ), those models can be categorized based on how we choose to represent the character Supervised.
We start with auntterance-based model, where we string together all utterances
spoken by the head and tail speakers, respectively. The motivation is that each speaker can be
represented by all of their utterances in the scene. Those utterances are subsequently encoded
by the same Longformer–base encode2]r; [we extract the CLS toke<nss>() that represent all
the utterances and concatenate them, resulting in the overall representation:
we introduce the following special tokens, whose representations are learned during training:
<scene>, <speaker>, <line>, and<background>. A full example is in Fig1. We take
inspiration from3[
if the -th word is spoken by either head or tail speaker an[d] = 1
otherwise. Followin3g5][, the scene information selecte d biys: ℎ = <s>; = ⊤ℎ ;
= softmax( ⊙ ).
The head- and tail-aware attention is used to pull the hiddeℎn st a,twesh: ich is concatenated with head and tail utterances: ⊤ ℎ = [ <ℎs> ; <s>; ℎ ⊤].
(2) (3) softmax( ( ℎ)) and ̂= arg max(⋅) .
The overall representatℎioisnthen fed to a linear classification h e,awdhich yields(| ℎ) =
Prompting. For prompt-based models, the overall prompt design resembles a QA task, where
given a scene, the model is tasked to anshweaedr,speaker is ____ of tail speaker. We consider
three popular strategies to enhance the performance of large language models: we prompt
LLaMA 3–70b–instruct zero-shot and one-s6haontd, leverage the hidden chain-of-thought
process [
Experimental results are reported in T1a,ibnlceluding 95% confidence intervals from 10,000 bootstrap resampl8esW. e evaluate the performance of each model by comparing its prediction against the true relationship label we have obtained from Wikipedia. Given the premise of this work is that the true relationship type is not necessarily performed in every interaction, we expect the accuracy here to be low, but the model should still perform better than guessing the most frequent label in the training set. In assessing the impact of anonymization, we include additional rows for when we anonymize the training set and columns for test ones.
For the supervised models, we observe that adding contextual information about the scene that is absent from the head and tail utterances significantly improves the performance of the model. When the model already has the scene information, anonymizing the training set appears beneficial, which is aligned with the findings reporte7d].inF[or evaluation, anonymization does not significantly hurt the performance of our supervised models. However, it does for the prompt-based models we evaluate. While they yield similar performance on the unanonymized data (arou0n.2d42), the three prompt-based models do not produce identical predictions: LLaMA’s zero-shot and one-shot models have a C o[h9e]no’fs0.71[0.70, 0.72], and that between LLaMA zero-shot and OpenAI o1-m0in.3i7i[s0.36, 0.39]. This suggests a low agreement rate between LLaMA and OpenAI models.
Since the goal of this section is to figure out the best strategy for modeling stereotyping readers, it is crucial to establish the facial validity of the best-performing model before we impart any trust in its predictions. In further examining the face validity of the predictions of our best-performing model before moving on to the analysis, we represent the most distinct tokens for the kepyredicted relationship types measured by log-odds ratio with an uninformative Dirichlet prior30[] in Table2, which we can consider a form of post-hoc global explanation providing insight into what a model has lea1r2n]e.dW[e aggregate all head utterances by the predicted relationship type and use them as the target corpus, and the rest as the reference corpus. For each relationship type presented, we include the top ten tokens most strongly asso6https://huggingface.co/meta-llama/Meta-Llama-.3-70B 7https://openai.com/index/openai-o1-mini-advancing-cost-efficient-rea.soning/ 8For supervised models, we use a learning rat5e×o1f0−5 with100 warm-up steps and no weight decay. All inputs are padded or truncated4,t0o96 tokens. All models are trained on four L40S GPUs. If the scene is long4e,r09t6han tokens, it is truncated before being inserted into the prompt (only four scenes were truncated). We use Outline to constrain the output space to be one of the relationshihptttyppse:s//:github.com/outlines-dev/out.lines ciated with the target corpus. Those five types are chosen because they are central to our case studies presented in the analysis section below. In2T, awbelesee that the model has learned to associate the relationship types with some of the words the dyad in question typically talks about (e.g. parents talk about kicdosll)e,ague has to do with occupational terms on TV (e.g. detectives investigate the death of someonef)r,ieannddis focused on high school life (e.g. students vote for student council president). Although there are terms that seem confusing (say, jacket incolleague_of) out of context, the words that are more strongly associated with those categories make an intuitive sense in the context of scripted TV series.
Well, I suppose I do think of you as a sister. And sometimes, a mother.
—Sheldon Cooper to his friend, Penny, inBtihgeBang Theory [O]ne of the things that “queer” can refer to: the open mesh of possibilities, gaps, overlaps, dissonances and resonances, lapses and excesses of meaning [ . . .].
—eve kosofsky sedgwick, Tendencies [
Our first case study takes up David Halperin’s inquiry qinuteeor love [
Season
e 50 tang
e 0 rcPe new forms of relationality: men in queer love need to “invent, from A to Z, a relationship that is still formles1s4”, [p. 136]. Can we use our model to investigate counter-conduct and relational forms? The titles in our analysis dataset feature some characters that have become the subject of various queer readings: for example, Frasier and NileFsrafsrieormare said to embody gay sensibility33[], and the four male protagonists ofBtighBeang Theory perform “queer-straight masculinit2y9”].[Juxtaposing the relational possibilities queerness afords and the subversive potentials that many find inherent in those characters, we can use our model as a tool to facilitate and augment close reading.
In theBig Bang Theory, Raj Koothrappali and Howard Wolowitz are colleagues at Caltech, but they often talk like a couple: howard. We’re just saying all the things we love about each other.
raj. Oh, like you and I did at couple’s therapy? (season 8, episode 9)
Much debate surrounding Raj focuses on whether he is gay, to which Steve Molaro, producer
and writer of the show, says: while it’s viable, “it was a little more interesting to have a guy
so comfortable in his feminine side who’s not gay, and explore3t2h,ap.t”24[
In Fig. 2, we chart the percentage of the dyad being predictcoeldleaagsues or spouses to each other across the eight seasons in our dataset of choice. It is not surpricsoilnlegatguoesee being the most prominent relationship type, but it gradually decrea7s0e.2s%frionmseason 1 to46.4% in Season 8, while thesp“ouse_of” relationship type is predicted starting from season 2, peaking a7t.4% in Season 7. This indicates a predominant colleague relationship with minor fluctuations towards a more intimate or spousal-like dynamic. While talking like spouses when they are colleagues can be seen as an act of transgression, do those characters
Niles Crane and Frasier Crane in Frasier e sibling_of 51.0 42.7 34.2 38.8 41.7 37.4 40.8 40.0 39.3 30.5 26.6 yTp parent_of 9.8 22.0 22.4 17.9 18.1 17.6 26.3 18.6 26.2 16.9 21.5 isnhpcolleague_of 13.7 14.6 23.7 6.0 13.9 17.6 19.7 20.0 18.0 25.4 24.1 iltoa child_of 15.7 9.8 10.5 13.4 13.9 9.9 6.6 10.0 9.8 10.2 11.4 eR spouse_of 9.8 7.3 5.3 19.4 8.3 6.6 2.6 7.1 1.6 10.2 11.4 1 2 3 4 5 6 7 8 9 10 11
Season 40 e 30 tang 20 rcee 10 P necessarily submit to the social institution of marriage in their form of co-existence, which thespouse category would entail? If we believe our stereotyping reader and assume that the spouse is an observable relational form between them, we ask: can there be subversion within subversion, and do Raj and Howard contest this normative relational form?
For that we return to close, suspcious reading. One relevant episode takes place towards the end the series (season 12, episode 22): We find Raj at an airport, waiting for his flight to London, where he is expected to meet with his girlfriend, Anu. And we see Howard in discussion on this with his wife, Bernadette: bernadette. Go stop him. Get your best friend back. howard. You are my best friend! bernadette. We don’t have time for this! Go! This exchange, along with the ensuing airport scene, stages a jubilant celebration of queer love. If, according to Halperin, “where the happy couple advances, deviance re1t7r,epa.t3s9”7[], thanks to Bernadette, we see this playing out in the opposite direction: the married couple steps aside, for queer love to flourish. Howard getting Raj back by no means signals the end of his marriage with Bernadette, but it surfaces the tension between traditional heteronormative relationships and non-normative desires: Howard’s dynamic with Raj reveals an undercurrent of emotional intimacy and dependency that challenges the rigid boundaries of what is considered acceptablneo,rmal male bonding within the confines of marriage. Bernadette, curiously, becomes a kind of referee, simultaneously reinforcing and undermining the normalcy of her marriage: She exposes the instability of the heteronormative relationship model, while also ensuring that it doesn’t just break apart. As Oscar Wilde puts it in his putatiEvaerlnyesqt,ueer “In married life, three is company and two is none.”
Now we return to Niles and Frasier Crane, to the question we raise at the beginning of this paper: If they are nojutst brothers, who are they to each other? It is not surprising th3a, t in Fig. the most salient relationship type for them is that of a sibling, but we see how they perform a few diferent ones over the course of the show. Towards the end, Nile appears to be just as much a parent and a colleague as a brother for Frasier. We see how they “invent” (in Foucault’s word) a new relational form for themselves as they oscillate between the relationship types we
Season e p y T ip child_of h s insibling_of o t a l e R e 40 tag n e 20 rcPe already have names for, although others, like Alison and Harry from Introduction, might find them odd—and, indeed, queer—at times.
Today,queer encompasses a broad spectrum of identities, practices, and desires related to sex and gender. But this is not the case for queer theory at its infancy: writing around 1993, Eve Kosofsky Sedgwick notes how queer scholarship “can’t be subsumed under gender and sexuality at all3”6[, p. 8]. In this section, we operate on this expanded notion of queer and turn to subversive modes of parenting.
Recall this line from Sect2io:“nGo to your room, Lorelai” (season 1, episode 4) is the daughter talking like a mother. This is an instance of subversion where traditional parent–child boundaries is blurred, as Rory no longer conforms to the conventional roles. We do see two modes of parenting depictedGinilmore Girls: the traditional model Lorelai and her mother, Emily Gilmore represents, and the new, subversive one between Rory and Lorelai4., In Fig. we chart the interaction patterns by the percentage of the predicted relationship type between these two pairs of mother and daughter each season, focused on the top three relationship types. According to our model, we see Emily, compared to Lorelai, is more of a traditional mother throughout, wiptahrent_of being the dominant relationship type. This is aligned with our impression with the characters: as Lorelai says, “Rory and I are best friends, Mom. We’re best friends first, and mother and daughter second. And you and I are mother and daughter always” (season 2, episode 16). However, as we see in4F,itgh.ey start to talk more like sisters around each other in later seasons. One episode where the dynamic between Lorelai and Emily changes, as identified by our model is “Friday and Alright For Fighting” (season 6, episode 13), where Emily says jokingly to Lorelai, “I only wished I’d remembered to call her a cocktail waitress!” This is a surprise to Lorelai: “That’s my mother’s versicownoorfdt!h”e
Another intriguing example of subversive parenting can be found between Sheldon Cooper and Penny in thBeig Bang Theory. In this show, Sheldon works with Raj and Howard at Caltech, ecolleague_of p T sibling_of y p isnh child_of litoa friend_of eR parent_of 30 taeg 20 rcen 10 Pe 0 and Penny represents the “cute girl next door next to the 1n3e]radrsc”h[etype: the main male cast routinely succumbs to her feminine charm, especially in early seasons, with the sole exception of Sheldon. For this reason, Sheldon is regarded as a2s9e]xauanld[infantilized38[]. The latter dimension of the character is explored throughout the series through Penny, which is the main plot of “The Intimacy Acceleration” (season 8, episode 15), from which the epigraph of this section is taken. In this episode, the two engage in a farcical experiment to find out if Sheldon and Penny can fall in love. For Sheldon, his announced goal is not to win the girl, but to have her drive him to a “convention celebrating the life and work of Gary Gygax.” Their experiment commences with Penny saying, “I will buy you all the dragon T-shirts you want.” For most of this episode, like a Kleinian b2a0b]y, S[heldon explores object relations and symbol formation in his phantasy-as-experiment: at the end of it, he compares himself to “human bowl of tomato soup”, and Penny to his sister and his mother.
Our model of stereotyping readers captures that, and more. The top relationship types for Sheldon and Penny do inclusdiebling_of andchild_of, and according to the model, one of the moments where Sheldon talks like Penny’s child involves her trying to put him to sleep, though unsuccessfully: “No, I don’t want to go to sleep, you can’t make me” (season 8, episode 13). What’s surprising here is Sheldon and Penny, much like Frasier and Niles, take up multiple relational roles over the eight seasons, and the most frequent of the mcoillsetaghuaets.oIff we revisit the series with a keen eye on when they speak like colleagues, we see how Penny can act like a counsel in Sheldon’s social and everyday life: on how to empathize with others (season 2, episode 3), girl trouble at a bar where Penny works (season 4, episode 17), and so on. Penny’s capacity for social mentoring reverses the normal dynamics between them, where Sheldon feels and acts like her superior. In terms of intertextuality, this can explain why Sheldon thinks of Penny as a sister: in the prequel to the seYroiuens,g Sheldon, we see his sister, Missy Cooper, possess extraordinary social intelligence.
Surprise is the operative word for this section, and this choice of words is intentional. Inspired by Melanie Klein’s work, including her theories on object relations and phantasy, Sedgwick developed her concept of repatative reading: “[T]o read from a reparative position is to surrender the knowing, anxious paranoid determination that no horror, however apparently unthinkable, shall ever come to the readneerw;atso a reparatively positioned reader, it can seem realistic and necessary to experience surpr3i7s,ep”.[146]. In being surprised, we see how computational methods can enable us to take the reparative position as we approach the text; we attempt to bring out the multiplicity of characters, like Emily and Penny, and begin to repair their agency. Indeed, they are much more than stereotypical mothers or stereotypical cute girls.
In this paper, we model a stereotyping reader who can infer the social relationship of pairs
of characters (or dyads) in conversation. As an algorithmic measuring device, this model is
queer in and of itself: the model tries to learn, for example, what a couple talks like, but we
are ultimately interested in finding characters thnaott aarceouple but talk like one, therein
lies the queerness we wish to explore. As such, metrics like accuracy (as we argue in3)Section
are at best an instrumental means, and the model is interesting (as we see in the case studies)
only when it predicts anythbinutg the truth. As queer studies intersect with computational
humanities, through our model, we wish to take initial steps to “dismantle the logics of success
and failure”1[5, p. 2] to resist the “normal business in the acade4m3,yp”.[ xxvi], and in so
doing, reflect on disciplinary practices in the relevant research communities to “build a better
description”2[
One of the powerful images that Sedgwick invokes in her work, which has ultimately altered
the landscape of queer theory permeably and forever, is that of a “theory kinde3r7g,arten” [
p. 94].9 As we gesture towards a potential future for a queer cultural a6n]a,wlyetilcoso[k
back on Sedgwick’s theory kindergarten. Sedgwick was critical, but we might think of a theory
kindergarten as a generative space of playful exploration, not bound by rigid epistemological
frameworks or normative, institutional constraints. As such, theory kindergarten can be as
inclusive, reflexive, and enabling: as we see Tinouching Feeling, it invites us to “think
otherwise” [37, p. 11]: embrace surprise and creativity, allow for new modes of understanding and
inquiry to emerge—perhaps including computation, without reducing the complexity and
multiplicity of queer thought. If relational forms that emerge out of dialogic interactions showcase
the “open mesh of possibilities” of queerne3s6s, [p. 7] that Sedgwick speaks of,
interdisciplinary conversations, as Bradway, the source of the epigraph for this section, so eloquently
intimates in the context of queer narrative theory, do not need an overdetermined terminus.
We hope this work can motivate more researchers to study the representation of subversion
in queer cultures and test the limits of both queer theory and computational methods. Back
in the kindergarten: experiment and play, think and operationalize otherwise, and reimagine
queerness vis-à-vis computatio10n.
9See also 4[], [
We thank the anonymous reviewers for their helpful comments. We are grateful for feedback provided by Richard So and Mackenzie Cramer at diferent stages of this project. The research reported in this article was supported by funding from the National Science Foundation (IIS1942591), the National Endowment for the Humanities (HAA-271654-20), and a UC Berkeley Paul Fasana LGBTQ Studies Fellowship to K. C. [31] T. Pugh. The Queer Fantasies of the American Family Sitcom. Rutgers University Press, 2018.
This work builds on prior research in several disciplinary traditions.
Sociolinguistics and linguistic anthropology. The study of social relationships through language is central to the fields of sociolinguistics and linguistic anthropology. These disciplines provide tools for analyzing how language constructs and reflects social identities and power dynamics. For this work, Michael Silverstein’s w39o, r4k0][, which explores the ways in which language ideologies and linguistic practices intersect, informs our approach to how social relationships are subverted and maintained through dialogue.
Dialogue understanding and natural language processing. The design and
implementation of our computational methods are indebted to the field of natural language processing
(NLP), especially work on dialogue understanding. Our main task is built on dialogue relation
extraction techniques, which are employed to classify the relationships between characters
based on their conversational exchan1g9e,2s2[]. This work also builds upon narrative
understanding and conversation modeling. This is exemplified by, among others, TVShow3G5u],ess [
which leverages neural models to perform reading comprehension tasks on television shows.
In the context of this work, understanding the hierarchical structure of dialogue is also
crucial 4[
TV and film studies. Much of this work aims to understand representation on screen, which is the subject of TV and film studie4s1[], and in particular, analyzing dialogue within TV and iflm is essential for understanding how social relationships are portrayed and subverted. This work is inspired by linguistic analysis in this sp34a,c1e][and works such as Sarah Kozlof’s21[] which look into the mechanics of scripted dialogue and its impact on the audience’s perception of characters and their relationships.
Gender and queer studies. The subversion of social relationships in TV often intersects
with issues of gender and sexuality. Judith Butler’s theories on gender performativity provide
the essential framework for understanding how characters on TV subvert traditional gender
roles and expectation5s].[The positivist approaches in queer stud1i6e,s2[
After the process described in Sectio2n,each pilot teleplay is now transformed into a sequence of scenes, which is comprised of speakers with canonical names and their lines, as well as, 1 grandparent_of 2 grandchild_of 3 parent_of 4 child-in-law_of 5 child_of 6 sibling-in-law_of 7 sibling_of 8 relative_of 9 ex-spouse_of 10 ex-boy/girlfriend_of 11 ex-love_interest_of 12 spouse_of 13 boy/girlfriend_of 14 love_interest_of 15 enemy_of 16 colleague_of 17 classmate_of 18 roommate_of 19 neighbor_of 20 teacher_of 21 student_of 22 boss_of 23 subordinate_of 24 trainer_of 25 trainee_of 26 acquaintance_of 27 friend_of 28 other where applicable, a list of relationship tuples for speakers in the scene. The statistics of title and token counts for this dataset are reported i4n.1T1able
See Fig. 6.
See Fig. 7 for the prompt, and Fi8gf.or an example response from ChatGPT o1-mini. The API to o1-mini does not include access to the tokens used for chain-of-thought, so this example is 11Tokens are counted with the tiktoken implementation of BPE took20en0kiz_ebrase: https://github.com/openai/ tiktoken/tree/m a.in "role": "system", "content": f"""Your goal is to extract relationships between TV characters in a
scene of a TV series.
You will be provided with their dialogues, wrapped in <dialogue>.
Speaker names start with `ENTITY`, and their lines are separated by `:`.
You will read the dialogue and identify the relationship between a certain pair of entities, as requested in <question>.
The relationship is directed, so the order of entities in each triplet matters.
Here are the possible relationship types: {LABEL_OPTIONS}.
Here is an example:""" }, { "role": "user", "content": f"""<dialogue>SCENE: INT. WEINBERG APARTMENT - MIDGE'S OLD BEDROOM
MOMENTS LATER ENTITY 24: That forehead is not improving. [ENTITY 24 lifts ESTHER out and lays her down on the bed.] ENTITY 2: What? Are you sure? ENTITY 24: It's getting bigger. The whole face will be out of proportion.
ENTITY 2: But look at her nose. It's elongating now, see? ENTITY 24: The nose is not the problem. The nose you can fix. But this gigantic forehead ...
ENTITY 2: Well, there's always bangs.
ENTITY 24: I'm just afraid she's not a very pretty girl.
ENTITY 2: Mama, she's a baby.
ENTITY 24: I just want her to be happy. It's easier to be happy when you're pretty. ENTITY 24: You're right. Bangs will help.</dialogue> <question> ENTITY 2 is what of ENTITY 24? ANSWER with ONLY {LABEL_OPTIONS} </question>""" }, { "role": "assistant", "content": "child_of" "role": "user", "content": f"""<dialogue>{scene_string}</dialogue> <question> {head} is what of {tail}? ANSWER with ONLY: {LABEL_OPTIONS}.</question>""" }, }, { "role": "system", "content": f"""Great job! You have successfully identified the relationship between the two entities. Now, let's move on to the next one.""" included as a sanity check and see the thinking process of o1 models where the thought process is visible to us. **Return only a JSON object** with the following property: - "answer": one of the following {LABEL_OPTIONS}.
This property must always be present.
Do not include any additional text or explanations outside the JSON object. <dialogue>SCENE: INT. WEINBERG APARTMENT - MIDGE'S OLD BEDROOM - MOMENTS LATER ENTITY 24: That forehead is not improving. [ENTITY 24 lifts ESTHER out and lays her down on the bed.] ENTITY 2: What? Are you sure? ENTITY 24: It's getting bigger. The whole face will be out of proportion.
ENTITY 2: But look at her nose. It's elongating now, see? ENTITY 24: The nose is not the problem. The nose you can fix. But this gigantic forehead ...
ENTITY 2: Well, there's always bangs.
ENTITY 24: I'm just afraid she's not a very pretty girl.
ENTITY 2: Mama, she's a baby.
ENTITY 24: I just want her to be happy. It's easier to be happy when you're pretty. ENTITY 24: You're right. Bangs will help.</dialogue> <question> ENTITY 2 is what of ENTITY 24?</question>"""
}