Conversational Context Research Articles

Cultural Shock and its Recovery as Seen through Language Features in Emily in Paris

This study aims to find out the language features used by Emily Cooper in the series Emily in Paris during the culture shock and recovery stages. In conducting this study, I used the theory of cross-cultural transition and adaptation stages by Oberg (1960), as cited in Marx (2001), and the theory of language features by Lakoff (2004) and Coates (2004). This study was done using a qualitative approach in which I transcribed and analyzed Emily Cooper’s utterances that occurred in all office scenes in Paris. Emily used adjectives, hedges, intensifiers, grammar, politeness, swear words, commands and directives, minimal responses, and questions language features during the culture shock stage. Emily used color terms, adjectives, hedges, intensifiers, grammar, politeness, swear words, minimal responses, and questions language features during the recovery stage. Emily’s personality, conversation context, and culture were variables that might influence Emily’s use of language features. Keywords: language features, cultural adaptation, culture shock, recovery

The Pandemic as a Portal: Reimagining Psychological Science as Truly Open and Inclusive

Psychological science is at an inflection point: The COVID-19 pandemic has exacerbated inequalities that stem from our historically closed and exclusive culture. Meanwhile, reform efforts to change the future of our science are too narrow in focus to fully succeed. In this article, we call on psychological scientists—focusing specifically on those who use quantitative methods in the United States as one context for such conversations—to begin reimagining our discipline as fundamentally open and inclusive. First, we discuss whom our discipline was designed to serve and how this history produced the inequitable reward and support systems we see today. Second, we highlight how current institutional responses to address worsening inequalities are inadequate, as well as how our disciplinary perspective may both help and hinder our ability to craft effective solutions. Third, we take a hard look in the mirror at the disconnect between what we ostensibly value as a field and what we actually practice. Fourth and finally, we lead readers through a roadmap for reimagining psychological science in whatever roles and spaces they occupy, from an informal discussion group in a department to a formal strategic planning retreat at a scientific society.

In your face? Exploring multimodal response patterns involving facial responses to verbal and gestural stance-taking expressions

In the present study, informed by insights from cognitive and interactional linguistics, we set out to explore how facial expressions systematically occur as responses in interactional sequences. More specifically, we use FACS-analyses (Facial Action Coding System) to study which Action Units (AU) on the part of the listener co-occur with multimodal stance-taking acts by speakers. Based on a data set of 24 dyadic interactions, we show that different types of stance acts (e.g. marking obviousness vs. using expressive amplifiers) reveal different patterns of facial responses. In addition, also within one type of stance act, there is systematic variation in facial responses. For example, listeners displayed significantly different AU-patterns in reactions to verbal obviousness markers, compared to non-verbal obviousness markers. Together, these observations highlight that, analogous to verbal responses in interactional sequences, also facial motor responses appear to be systematic, and highly dependent on conversational context. As such, the AU's under scrutiny serve as intersubjectively aligned response turns completing a situationally designed stance-taking act. With this interdisciplinary study, combining linguistics with psychology and physiology, we aim for a better understanding of the multimodal complexity that constitutes the process of meaning making in spontaneous conversation.

Dynamic Graph Reasoning for Conversational Open-Domain Question Answering

In recent years, conversational agents have provided a natural and convenient access to useful information in people’s daily life, along with a broad and new research topic, conversational question answering (QA). On the shoulders of conversational QA, we study the conversational open-domain QA problem, where users’ information needs are presented in a conversation and exact answers are required to extract from the Web. Despite its significance and value, building an effective conversational open-domain QA system is non-trivial due to the following challenges: (1) precisely understand conversational questions based on the conversation context; (2) extract exact answers by capturing the answer dependency and transition flow in a conversation; and (3) deeply integrate question understanding and answer extraction. To address the aforementioned issues, we propose an end-to-end Dynamic Graph Reasoning approach to Conversational open-domain QA (DGRCoQA for short). DGRCoQA comprises three components, i.e., a dynamic question interpreter (DQI), a graph reasoning enhanced retriever (GRR), and a typical Reader, where the first one is developed to understand and formulate conversational questions while the other two are responsible to extract an exact answer from the Web. In particular, DQI understands conversational questions by utilizing the QA context, sourcing from predicted answers returned by the Reader, to dynamically attend to the most relevant information in the conversation context. Afterwards, GRR attempts to capture the answer flow and select the most possible passage that contains the answer by reasoning answer paths over a dynamically constructed context graph . Finally, the Reader, a reading comprehension model, predicts a text span from the selected passage as the answer. DGRCoQA demonstrates its strength in the extensive experiments conducted on a benchmark dataset. It significantly outperforms the existing methods and achieves the state-of-the-art performance.

“Immersed in World of Warcraft”: A Discursive Study of Identity Management Talk About Excessive Online Gaming

Online excessive gaming has been associated with negative player identity constructions depicting an abnormal life-style. Up-to-date, there is limited insight into player identity management talk about excessive online gaming. To address this gap, drawing from discursive and rhetorical psychology, we investigated naturally occurring talk of 134 players of World of Warcraft (WoW) -a Massively Multiplayer Online Role-Playing Game (MMORPG)- from three publicly available websites and of five players from one focus group. The analysis illuminated participants’ dilemmatic and contradictory ways of constructing the player identity, while displaying immersion in the game. Participants invoke identity constructions like ‘nolifer’, ‘hardcore’ or ‘clean’ player, which they disavow or assign to themselves and to each other depending on the conversational context, while attending to concerns about (ab)normalcy. The study’s findings highlight a dynamic process of player identity construction in talk, occasioned by and exemplifying the contingencies of the discursive context.

On the expansion of biological functions of lytic polysaccharide monooxygenases.

Lytic polysaccharide monooxygenases (LPMOs) constitute an enigmatic class of enzymes, the discovery of which has opened up a new arena of riveting research. LPMOs can oxidatively cleave the glycosidic bonds found in carbohydrate polymers enabling the depolymerisation of recalcitrant biomasses, such as cellulose or chitin. While most studies have so far mainly explored the role of LPMOs in a (plant) biomass conversion context, alternative roles and paradigms begin to emerge. In the present review, we propose a historical perspective of LPMO research providing a succinct overview of the major achievements of LPMO research over the past decade. This journey through LPMOs landscape leads us to dive into the emerging biological functions of LPMOs and LPMO-like proteins. We notably highlight roles in fungal and oomycete plant pathogenesis (e.g. potato late blight), but also in mutualistic/commensalism symbiosis (e.g. ectomycorrhizae). We further present the potential importance of LPMOs in other microbial pathogenesis including diseases caused by bacteria (e.g. pneumonia), fungi (e.g. human meningitis), oomycetes and viruses (e.g. entomopox), as well as in (micro)organism development (including several plant pests). Our assessment of the literature leads to the formulation of outstanding questions, promising for the coming years exciting research and discoveries on these moonlighting proteins.

On the Natural Ground of Discursive Cognition

Despite increasing interest in shared intentionality in both philosophy and the sciences over the last three decades, there has been little comparison of philosophical with empirical accounts of the phenomenon. At the same time, both philosophical and scientific investigations into shared intentionality as a ground of our cognition have developed into widespread research programs during this period. This has laid the groundwork for a productive conversation, across the sciences and humanities, about the nature of human cognition qua discursive or rational. In this essay, I map some of the conceptual terrain such a conversation would cover, and I consider some of the extant efforts to build explanatory bridges across research and conversational contexts—using the resources of one domain of understanding to help structure our understanding of another—to the benefit of both philosophical and scientific approaches to the study of human cognition.

A Corpus-Based Pilot Study of the Pop Lyrics on Billboard

Although pop music is endorsed as one of the most leisure activities to express feelings and emotions in people’s daily life, song lyrics are usually neglected by linguists and excluded in any of English corpora (e.g., BNC and ANC) that are currently adopted. The current study focuses on an analysis of lyrics based on a Billboard music chart corpus mainly containing American pop lyrics, via both qualitative and quantitative methods, and aims to investigate to what extent the lexico-grammatical features of the words and expressions used in lyrics enable those songs widely accepted by audience. Results reveal that conversational contexts in the lyrics mark a story-telling feature, and help audience easily realize artists’ attitudes and the implications to some events and scenarios.

Translanguaging space and translanguaging practices in multilingual Russian-speaking families

Translanguaging is seen both as a threat and as an opportunity for minority language development and transmission. While the theme of translanguaging has been explored especially in a context of migration, the novelty of this study lies in its investigation of the multiple contexts in which translanguaging is examined. In order to understand the nature of translanguaging, we adopt a novel interdisciplinary approach and view it in all its complexity, including liminal spaces of linguistic landscape. Family language policy affects the home linguistic environment. Our purpose is to investigate language choices by multilingual Russian-speakers in Cyprus, Sweden and Estonia, immigrant and minority settings, and try to understand how they are reflected in the multilingual interaction of the families. Using ethnographic participant observations and oral spontaneous multilingual production, our study attempts to describe how communication is managed through translanguaging practices among multilingual Russian-speaking families members in the cultural and linguistic environments of the three countries. By looking closely at the complexities of translanguaging space, it is our ambition to gain new insights about how it is organised and how translanguaging becomes a valuable linguistic resource in multilingual families. Our results indicate that translanguaging practices can be used in family conversational contexts and contribute to the creation of a rich and positive family repertoire. A new norm of Russian has been developed in all the three settings. A language shift can happen more quickly than expected, and, thus, it is important for parents to provide many opportunities for practising Russian as the L1.

Deutsches auch und italienisches anche im Vergleich

The focus of our contribution is the German particle auch ‘also’ and its Italian counterpart anche. From a semantic point of view, auch and anche convey the same additive meaning in both languages: through them an element, called ‘domain of association’, is added to a list of further elements for which the auch/anche phrase applies. From a syntactical point of view, auch and anche can occupy different positions in the sentence by modifying the type and the scope of the domain of association. This results in four basic usage patterns which are similar in both languages and correspond to just as many functions of auch/anche: (i) additivity, (ii) sentence connection, (iii) focus domain, and (iv) modality. Despite these common functions, auch and anche interact with the syntax and the information structure of the sentence in German and in Italian differently. Furthermore, they are used in conversation contexts, which are distinctive in both languages.
 By starting from the current description of auch and anche in the literature, the first aim of our contribution is to provide evidence for the above-mentioned four basic functions despite their language-specific usage contexts. As a second aim, we want to validate our systematization through the analysis of spoken data for German and Italian, explaining why some functions are more likely to be found in the former than in the latter.

Linguists study language structure

To study linguistics is to gain a greater understanding of a fundamental part of what it means to be human. Linguistics is a scientific field and an academic discipline that has both theoretical and practical applications. Linguists study language structure at several theoretical levels that range in size from tiny units of speech sounds to the context of an entire conversation. Students of linguistics often begin with a basic understanding of each level of language, then specialize in one or more levels or in a practical application of linguistics. The smallest units of language are studied in the field of phonetics, which concerns itself with the individual sounds produced while speaking. Phonology takes a look at those small units of sound together in the context of whole utterances, and searches for patterns in sound across a language or a whole group of languages.

Modeling Global and Local Interactions for Online Conversation Recommendation

The popularity of social media platforms results in a huge volume of online conversations produced every day. To help users better engage in online conversations, this article presents a novel framework to automatically recommend conversations to users based on what they said and how they behaved in their chatting histories. While prior work mostly focuses on post-level recommendation, we aim to explore conversation context and model the interaction patterns therein. Furthermore, to characterize personal interests from interleaving user interactions, we learn (1) global interactions , represented by topic and discourse word clusters to reflect users’ content and pragmatic preferences, and (2) local interactions , encoding replying relations and chronological order of conversation turns to characterize users’ prior behavior. Built on collaborative filtering, our model captures global interactions via discovering word distributions to represent users’ topical interests and discourse behaviors, while local interactions are explored with graph-structured networks exploiting both reply structure and temporal features. Extensive experiments on three datasets from Twitter and Reddit show that our model coupling global and local interactions significantly outperforms the state-of-the-art model. Further analyses show that our model is able to capture meaningful features from global and local interactions, which results in its superior performance in conversation recommendation.

Does it matter whether we or I talk aboutus? Distinguishing we-talk in couples’ conflict discussions and partners’ private thoughts before and after conflict

According to extensive evidence, we-talk—couples’ use of first-person, plural pronouns—predicts better relationship quality and well-being. However, prior work has not distinguished we-talk by its context, which varies widely across studies. Also, little is known about we-talk’s consistency over time. To assess the stability and correlates of we-talk in private versus conversational contexts, 43 married couples’ language was captured during a marital problem discussion and in each partner’s privately recorded thoughts before and after conflict. Participants were asked to describe any current thoughts and feelings in the baseline thought-listing and to focus on their reaction to the conflict itself in the post-conflict sample. Couples repeated this protocol at a second study visit, approximately 1 month later. We-talk in baseline and post-conflict thought-listings was largely uncorrelated with we-talk during conflict discussions, but each form of we-talk was consistent between the two study visits. Their correlates were also distinct: more we-talk during conflict was associated with less hostility during conflict, whereas more baseline we-talk predicted greater closeness in both partners, as well as lower vocally encoded arousal and more positive emotion word use in partners after conflict. These novel data reveal that we-talk can be meaningfully distinguished by its context—whether language is sampled from private thoughts or marital discussions, and whether the study procedure requests relationship talk. Taken together, these variants of we-talk may have unique implications for relationship function and well-being.

Visual information and communication context as modulators of interpersonal coordination in face-to-face and videoconference-based interactions

Interpersonal coordination of body movement—or the similarity in patterning and timing of body movement between interaction partners over time—is a well-documented phenomenon in face-to-face (FTF) conversation. The present study will investigate the degree to which interpersonal coordination is impacted by the amount of visual information available and the type of interaction conversation partners are having. To do so within a naturalistic context, we take advantage of changes induced by the 2020 COVID-19 pandemic. The pandemic has changed communication, with mitigation efforts having forced nearly everyone to engage over videoconferencing (VC) platforms (which limit body visibility but not face visibility) or to meet FTF with public health constraints (which limit face visibility but not body visibility). We will ask 69 pairs of participants to communicate in one of three ways: (1) socially distanced FTF while wearing masks; (2) VC in a laboratory where each partner will see one another's full torso; or (3) VC in a remote setting where each partner will see only one another's face and shoulders. Each pair will hold three conversations: (a) affiliative, (b) argumentative, and (c) task-based. We will quantify interpersonal coordination by extracting overall amounts of movement from videos of the participants using well-validated computer vision methods and then calculating the relationship between the two participants' movement using nonlinear time series analyses. In doing so, we will be able to identify the degree to which visual information and conversational context shape the emergence of interpersonal coordination within now-naturalistic modes of interaction.

What Helps Transformers Recognize Conversational Structure? Importance of Context, Punctuation, and Labels in Dialog Act Recognition

AbstractDialog acts can be interpreted as the atomic units of a conversation, more fine-grained than utterances, characterized by a specific communicative function. The ability to structure a conversational transcript as a sequence of dialog acts—dialog act recognition, including the segmentation—is critical for understanding dialog. We apply two pre-trained transformer models, XLNet and Longformer, to this task in English and achieve strong results on Switchboard Dialog Act and Meeting Recorder Dialog Act corpora with dialog act segmentation error rates (DSER) of 8.4% and 14.2%. To understand the key factors affecting dialog act recognition, we perform a comparative analysis of models trained under different conditions. We find that the inclusion of a broader conversational context helps disambiguate many dialog act classes, especially those infrequent in the training data. The presence of punctuation in the transcripts has a massive effect on the models’ performance, and a detailed analysis reveals specific segmentation patterns observed in its absence. Finally, we find that the label set specificity does not affect dialog act segmentation performance. These findings have significant practical implications for spoken language understanding applications that depend heavily on a good-quality segmentation being available.

Turn-end Estimation in Conversational Turn-taking: The Roles of Context and Prosody

ABSTRACT This study investigated the role of contextual and prosodic information in turn-end estimation by means of a button-press task. We presented participants with turns extracted from a corpus of telephone calls visually (i.e., in transcribed form, word-by-word) and auditorily, and asked them to anticipate turn ends by pressing a button. The availability of the previous conversational context was generally helpful for turn-end estimation in short turns only, and more clearly so in the visual task than in the auditory task. To investigate the role of prosody, we examined whether participants in the auditory task pressed the button close to turn-medial points likely to constitute turn ends based on lexico-syntactic information alone. We observed that the vast majority of such button presses occurred in the presence of an intonational boundary rather than in its absence. These results are consistent with the view that prosodic cues in the proximity of turn ends play a relevant role in turn-end estimation.

On the Acquisition of Attitude Verbs

Attitude verbs, such as think, want, and know, describe internal mental states that leave few cues as to their meanings in the physical world. Consequently, their acquisition requires learners to draw from indirect evidence stemming from the linguistic and conversational contexts in which they occur. This provides us a unique opportunity to probe the linguistic and cognitive abilities that children deploy in acquiring these words. Through a few case studies, we show how children make use of syntactic and pragmatic cues to figure out attitude verb meanings and how their successes, and even their mistakes, reveal remarkable conceptual, linguistic, and pragmatic sophistication.

Who Forgoes Screening in Online Markets and Why? Evidence from Airbnb

Screening is considered a necessary mechanism for alleviating information asymmetry but has also raised concerns about increased discrimination in online peer-to-peer market platforms. Paradoxically, providers of goods and services may also voluntarily forgo screening, even though it increases the risks and costs associated with poor matches. We examine who may choose to forgo screening and why, and its impact on their performance outcomes. Our study’s empirical context is the Airbnb platform, wherein the “Instant Book” feature enables hosts to forgo the screening of guests. Utilizing a unique panel dataset of all listings in New York City during August 2015-February 2017, we first explore the antecedents of voluntarily switching to instant booking and then causally identify the impacts of switching. Our study provides evidence of the economic benefits of forgoing screening from increased occupancy even as review ratings decline; these effects are stronger for Black and female hosts. We discuss the strategic and social welfare implications of these findings within the context of current conversations on discrimination and bias in the sharing economy.

Abusive language detection in youtube comments leveraging replies as conversational context.

Nowadays, social media experience an increase in hostility, which leads to many people suffering from online abusive behavior and harassment. We introduce a new publicly available annotated dataset for abusive language detection in short texts. The dataset includes comments from YouTube, along with contextual information: replies, video, video title, and the original description. The comments in the dataset are labeled as abusive or not and are classified by topic: politics, religion, and other. In particular, we discuss our refined annotation guidelines for such classification. We report a number of strong baselines on this dataset for the tasks of abusive language detection and topic classification, using a number of classifiers and text representations. We show that taking into account the conversational context, namely, replies, greatly improves the classification results as compared with using only linguistic features of the comments. We also study how the classification accuracy depends on the topic of the comment.

Facilitating marginalized youths' identification with STEM through everyday science talk: The critical role of parental caregivers

AbstractAn individual's sense of themselves as a “STEM person” is largely formed through recognition feedback. Unfortunately, for many minoritized individuals who engage in STEM (science, technology, engineering, and mathematics) in formal and informal spaces, this recognition often adheres to long‐standing exclusionary expectations of what STEM participation entails and institutionalized stereotypes of what it means to be a STEM person. However, caregivers, who necessarily share cultural backgrounds, norms, and values with their children, can play an important role in recognizing their children's interest and inclination towards STEM in ways that support children's authoring of their STEM identity in the face of these marginalizing discourses. To explore this idea, we conducted phenomenological interviews with STEM students attending a Hispanic‐serving university, examining the nature of STEM‐related conversations these students had with their parents during childhood. Participant recollections provide evidence of conversational content, contexts, and structures that supported their identification with STEM even when faced with marginalizing experiences. We found that though this phenomenon was recounted across parent profiles, participant narratives also reflected differences in conversation content, context, and structure based on factors associated with STEM stereotypes, including gender, formal education or training in STEM, and parents' immigration experiences. Viewed within larger sociocultural discourses of whose knowledge “counts” in STEM, our work suggests the need for educational institutions to acknowledge and embrace families' ability to foster youths' affinity with STEM contexts, while also recognizing and responding to institutionalized impediments to authentic STEM participation.