Individual Interaction Styles: Evidence from a Spoken Chat Corpus (2021)

Abstract: here is increasing interest in modeling style choices in dialog, for example for enabling dialog systems to adapt to their users. It is commonly assumed that each user has his or her own stable characteristics, but for interaction style the truth of this assumption has not been well examined. I investigated using a vector-space model of interaction styles, derived from the Switchboard corpus of telephone conversations and a broad set of prosodic-behavior features. While most individuals exhibited interaction style tendencies, these were generally far from stable, with a predictive model based on individual tendencies outperforming a speaker-independent model by only 3.6%. The tendencies were somewhat stronger for some speakers, including generally males, and for some dimensions of variation.

## introduction to create dialog systems that are able to work very well for any user will require modeling and adapting to individual interaction styles @xcite . for example, @xcite demonstrated a siri extension to detect which users are more talkative and then provide them information in a more chatty style. sociolinguists, going back to @xcite , have identified other ways in which people vary in interaction styles, such as focus on content vs interpersonal involvement, and domineering vs meek, among many others. a general assumption, implicitly underlying much work across the broad area of user modeling and adaptation, is that that each user has consistent behavior tendencies. but how true is this for interaction styles? while variation and adaptation have been studied for many specific components -including utterance selection, lexical choice, speech synthesis, paralinguistic and turn-based prosody, and language generation @xcite @xcite @xcite @xcite ) -the overall question seems not yet to have been examined. thus this paper addresses, the questions of whether individual interaction styles exist and how much they explain. i also examine gender differences in style and adaptation, and other related questions. ## data work on individual differences in dialog has been limited, mostly using data sets with only a few dozen participants, and mostly considering only tightly structured dialogs, mostly task-oriented, but more speakers and more variety can lead to more general models. most work has been limited to text or transcripts, but spoken data can be more informative. for these reasons i chose to use the switchboard corpus of american english telephone conversations @xcite . interaction styles are not instantaneous, but nor are they constant over long times, so i chose 30-second fragments as the unit of analysis. this seemed appropriate for a first study, and well-suited to switchboard, where the topic, tone, and style often shift from minute to minute. leaving some conversations for future validation work, i used a set of 33022 fragments, including 335 speakers. ## markers of interaction style there are many possible choices for markers of interaction style. like much previous work, i wanted to include prosodic features and features of turn-taking behavior @xcite @xcite , in part because being densely present, unlike word frequencies, they make analysis easier. however, wanting to consider more information, i created a more inclusive set to track various prosodic behavior frequencies, including those relating to a wide range of dialog states, activities, and events, including many of those often considered most important in human interaction (couper-kuhlen and selting, 2018), such as the extent and timing of turn holding, turn-taking, filler use and backchanneling; topic opening, development, and closing; bids for empathy; making positive and negative assessments; marking contrast; and so on. the specific features were based on a prosodic constructions model @xcite , in part because this enabled the use of a tool for automatic feature computation, including proper speaker and track normalization @xcite . the feature computation starts by computing the quality of the match between each prosodic construction's prototypical configuration and the actual behavior of the interactants, every 20 milliseconds across each conversation fragment. next, for each fragment, it computes the frequencies of occurrence for seven match-quality bins. for example, the fraction of timepoints at which the enthusiastic overlap construction is strongly matching indicates the frequency of strong engagement, the fraction where it is weakly present indicates the frequency of mild engagement, and the fraction where there is no evidence for it indicates the prevalence of lack of engagement. together these bin frequencies represent the extent to which the speakers are engaged in various interaction routines and the extent to which the dialog tends to dwell in certain states. with 12 prosodic configurations and 7 bins each, this gave 84 features per fragment. ## the space and the dimensions given these 84 features, each fragment can be represented as a point in a 84-dimensional vector space. while hopeful that this space corresponds well with the perceptual space of interaction styles, for lack of previous work on perceptions of styles, i can here only present indirect evidence. for current purposes, the most desirable property of this space is for fragments perceived closer in style to be closer in this space. spot checking a few of the pairs that were closest in this space confirmed that each pair was indeed very similar in style. another desirable property is interpretability. here, following @xcite , i choose to apply principal component analysis to the data, expecting that the resulting dimension would be meaningful, thereby providing further evidence for the relevance of this space. full discussion of the meanings of these dimensions will appear in another publication, but, in short, the top 8 dimensions indeed turned out to be meaningful, as revealed by good correlations with topics, lexical frequencies, and liwc word categories frequencies. one pole of dimension 3 relates to a negative stance, with clear lexical tendencies: for example gang, gangs, convicted, stole, offense, and disagree all occurring over 3 times more commonly in these fragments. topics in fragments near this pole were overwhelmingly things the speakers were not happy about, such as income tax, lawn problems, the futility of overseas aid, and time flying by. prosodically, there is an overall lack of normal turn taking, with frequent long silences often serving to mark how breathtakingly inappropriate something was, for example the mathematical ignorance of junior college students, and frequent overlaps, often wryly sympathetic laughter. this style is also rich in the prosody of topic continuation and topic develop- for dimension 6, one pole involves a style of accepting things beyond individual control. this can involve situations like living in a small town where the big touring bands never come, or a new corporate promotion policy, or the prevalence of gunsafety carelessness in the population. the prosodic tendencies are complex, but the most salient is the frequent occurrence of fairly lengthy silences. the lexical tendencies are also diverse, but relatively common words include nope, uncomfortable, and weeds. conversely the other pole exhibits topic continuation prosody and a general lack of turntaking, and relates to envisioning positive change. working in a reduced dimensionality space has numerous advantages, so for the analysis below i focused on just the top 8 dimensions. checking the relationship between perceptual similarity and proximity in this simplified space, again by examining the closest pairs; again these were perceptually similar, and this was true in diverse regions of the space, for example, for reminiscing about childhood situations that were annoying at the time but now seem nostalgic, with the interlocutor supportively showing empathy based on similar experiences; for jumping right in to address the assigned topic with a near monologue, with the interlocutor just occasionally chiming in with agreement; and for explaining political or commercial policies that the interlocutor is also familiar with and views in the same way. ## measure and models adaptive dialog systems need to predict what interaction style will be most appropriate for an upcoming dialog. using speaker information should enable more accurate predictions, if indeed interaction styles are stable properties of individuals @xcite . the vector space representation of styles enables us to measure the dis-tance between any two interaction styles, and in particular, between a predicted style and the observed style. this can serve as a metric for the evaluation of predictive models of interaction style. specifically, i use the mean squared difference for each dimension, and also the euclidean distance across dimensions. while i report distance results below using only the top 8 dimensions, with all 84 the results were very similar. the baseline model is to predict the global average style for every fragment. the model exploiting individual information predicts the interaction style as the average of the interaction styles in other fragments with one of the participants, excluding fragments from the same dialog. the models were evaluated using only fragments for which the 33022fragment subset included at least 20 others by the same speaker in different conversations, that is, at least 10 minutes of reference data for independent estimation of the individual's style. there were 31931 such fragments. ## results the first row of however, predictability varied across speakers. some were highly predictable: at one extreme, one speaker's mean distance for predictions was only 50% of the average (she consistently took a passive listening role); at the other extreme, one speaker's mean distance was over 4 times the average. overall, speaker-specific knowledge enabled better predictions for 78% of the speakers. digressing slightly, as entrainment in general takes time @xcite , one might expect that fragments taken from later into the calls would be closer to the participants' "true" styles, as they come to discover, reveal, relax into, and compromise towards their preferred styles. i therefore hypothesized that the styles of later fragments would be more predictable, but this turned out not to be the case. ## demographic differences the remaining rows of since women are often said to take more of the burden of adapting to their interlocutor, i hypothesized that women would generally exhibit more style variation than men. the average prediction error reduction obtained by using the individual models for women was 2.1% and for men 6.1%, so the women did indeed diverge more from their average styles. although the subpopulation means had little predictive power, it is interesting to consider what the per-dimension tendencies suggest. i examined four splits of the 33022 fragments: by gender, by age group, by order of joining the call, and by time into the call. statistically, fragments with women participating tend to more engaged, negative, and factual styles (dimensions 1, 3, and 5, effect sizes .16, .16, and .22 standard deviations, respectively). fragments with the older speakers tend to be more negative, and the older speakers tend to a more dominating style (dimensions 3 and 4, .13 and .10). fragments later in the conversation, specifically those occurring after 4 minutes in, tend to be more negative (.14). the speaker who joined the conversation first tended slightly to talk more and to dominate (dimensions 2 and 4, .04 and .05), which makes sense, as they were instructed by the robot operator to "please think about the topic while i locate another caller" @xcite , which sometimes took several minutes. all of these differences are statistically significant (@xmath0.0005, two-sided, unmatched-pairs, t-tests with bonferroni correction). ## discussion while there was evidence that most individuals have their own interaction styles, these explained little, reducing the error of style predictions by only 3.6%. this implies that the styles are not very stable: that individuals vary greatly in style. even if we could somehow create systems as good as the participants in this corpus at adapting their style to their interlocutor, they would generally perform only 3.6% better than systems that did not bother. while this result came as a suprise to me, it is not really hard to understand; in real life we know that how people talk varies with the situation, topic, interlocutor, time of day, and other factors. this suggests that future research on interaction style adaptation for spoken dialog systems should prioritize adaptation to factors such as the topic, situation, and dialog activity type, rather than adaptation to the user. other surprises include the finding that gender explains very little of the variation in interaction styles, and the finding that the most stable aspect of interaction style is the extent to which the speaker tends to a positive or negative stance. these findings and interpretations are tentative. future work should examine the generality of this finding, with more features, various fragment sizes, more powerful models, and larger and more diverse data, including text-only dialogs. future work should also examine not only behaviors but also preferences: although people in these conversations exhibited a variety of styles, perhaps, as users, people would prefer dialog systems that consistently use a fixed, individually-congenial interaction style. examining this might further lead to a detailed understanding of preferences, leading ultimately to individualized mappings from system behavior to satisfaction properties @xcite . finally, future work should include empirical explorations of human perception of the space of interaction styles. to support such work, the code for the investigations so far is available at https:// github.com/nigelgward/istyles .