Informal social interaction is the primordial home of human language. Linguistically diverse conversational corpora are an important and largely untapped resource for computational linguistics and language technology. Through the efforts of a worldwide language documentation movement, such corpora are increasingly becoming available. We show how interactional data from 63 languages (26 families) harbours insights about turn-taking, timing, sequential structure and social action, with implications for language technology, natural language understanding, and the design of conversational interfaces. Harnessing linguistically diverse conversational corpora will provide the empirical foundations for flexible, localizable, humane language technologies of the future. The primary ecology of natural language is in real-life episodes of human interaction. This is where people learn language and where they use it to coordinate joint actions, build social relations, and exchange information (Schieffelin and Ochs, 1986; Schegloff, 2006). In contrast, when machines encounter language, it tends to be radically divorced from this habitat and reduced to large amounts of decontextualised non-interactive text (Bender and Koller, 2020; Marge et al., 2022). Natural languages are also characterized by diversity at many levels, from sound and sign systems to syntax and semantics (Nettle, 1999; Evans and Levinson, 2009). In contrast, the language samples that inform language technology tend to be limited to a handful of well-resourced languages, representing only a tiny sliver of the world's linguistic diversity (Blasi et al., 2021; Joshi et al., 2020). Insights from such data can strengthen the empirical foundations of language technology, help break down the hegemony of the resourceful few, and provide room for linguistic diversity in localized applications (Bird, 2020; Danielescu and Christian, 2018). Today there is a growing set of conversational corpora of diverse languages, thanks in large part to important primary work on language documentation and description (Seifart et al., 2018). We argue such corpora represent an important and mostly untapped resource for language technology. Corpus size is often seen as a challenge, but data comes in levels of granularity. A well-curated corpus amounting to an hour of lively conversation may not contain enough text to train a language model. But it does provide thousands of conversational turns organized in larger sequential structures of social action, along with fine details about timing, participation and linguistic structure. Since conversational corpora are one of the few places where we can study language in a way that approaches its natural habitat, collectively, these corpora harbour important insights about human interactional infrastructure. Recent work has shown the dire state of language resources in relation to linguistic diversity (Blasi et al., 2021; Joshi et al., 2020), and pointed to ways forward to increase the empirical coverage of language typology and technology (Asgari and Schtze, 2017; Bjerva and Augenstein, 2018; Deri and Knight, 2016; Duong et al., 2015; Levow et al., 2021). Work in distributional and corpus-based typology is showing how to analyse linguistic information available in text corpora (Ponti et al., 2019; Seifart et al., 2021; Levshina, 2021). Compared to text corpora, conversational corpora are much harder to collect, annotate and transcribe, and as a result they represent a much smaller subset of data. However, we think there is reason for cautious optimism. In this paper we present a first foray into this domain. We collate conversational corpora made available for research purposes and find there is now data available for a wide range of languages, many of them not the usual suspects of NLP research. Besides well-known resources like TalkBank and the Linguistic Data Consortium, here we highlight the potential of corpora collected and archived as part of language documentation projects around the world (see Appendix B). Our focus is specifically on corpora of informal conversations among co-present participants, transcribed and time-aligned at the level of conversational turns. Details of our curation and analysis pipeline are described in the Appendix and in Liesenfeld & Dingemanse (2022). While it is impossible to exhaustively list or estimate the size of extant conversational corpora, the quality-controlled subset we consider here represents 63 languages from 26 language families (Figure 1), and amounts to over 800 hours of talk produced by over 11.000 partipants, segmented into over 1.6 million turns (9.3 million words) (Figure 2). In what follows, we examine aspects of this collection with scientific and technological applications in mind. In doing so, we aim to contribute towards a move from the most represented to a more representative sample of the world's languages, and to show how the study of human interaction can yield insights of relevance to linguistics, language technology and human-computer interaction. Despite recent advances in speech and dialogue modelling, to date, no machine can lead a half-decent coherent conversation with a human (Kopp and Krmer, 2021). There are several reasons for this, including the need for complex cognitive skills like intention attribution and incremental common ground construction, but equally important is a dearth of data and domain knowledge: modern natural language processing predominantly deals with text, not talk. As a simple illustration of the difference, compare frequency distributions of words and phrases in corpora of talk versus text in English, an Indo-European language (Figure 3). The forms most characteristic of talk are interactive interjections like hm, uhhuh, um, yeah, okay . Items like this 5615 Figure 3: Words and phrases characteristic of spoken interaction (green) versus written text (purple) in English, with words most characteristic of conversational interaction in the upper left. streamline conversation, calibrate mutual understanding and coordinate joint action (Clark, 1996; Bavelas et al., 2000). Yet it is precisely such items that are woefully underrepresented in the data underlying most current language models (Prevot et al., 2019). It is little surprise that conversational agents have a hard time dealing with informal conversational style (Hoegen et al., 2019) and building social bonds (Cassell, 2020), and that speech recognition easily mixes up interjections with opposite pragmatic functions (Zayats et al., 2019) if it doesn't miss them altogether (Cumbal et al., 2021). Proposed solutions to such challenges involve imparting agents with domain-specific interactional knowledge like keyword-based scripted conversational routines and domain knowledge from Q&A databases (Bocklisch et al., 2017; Dinan et al., 2019) or with capacities for feedback generation (Oertel et al., 2016) or common ground reasoning (Kopp and Krmer, 2021). Here we propose a complementary approach: pay closer attention to how language is used in informal everyday interaction around the world. We believe this is important because just like natural language processing has long been limited to monologic texts, the science of human interaction has for the most part been based just on English and a small number of similarly well-resourced languages (Henrich et al., 2010). If language technology is to be maximally scalable, localizable and usable, it will greatly benefit from broadening its empirical base towards more interactive data from a wider range of languages. Such data can improve our understanding of interactional infrastructure and can help us chart both language-specific routines and pragmatic universals of interaction. Utterances are not just strings with probability distributions defined over them; they stand in relation to other turns, with which they form structured sequences and implement social actions. A key element of this is a socially sanctioned turn-taking system by which participants self-organize the distribution of turns over participants (Sacks et al., 1974). Foundational work on English showed that participants appear to avoid both gaps and overlaps, often achieving speaker transition in as little as 200 ms. This temporal organization is so tight that it has long puzzled psycholinguists, who observe that even planning a simple sentence in isolation may take up to 600ms, implying that language comprehension and production must run in parallel (Levinson, 2016). Indeed participants do not wait for pauses to begin their contribution, but instead start planning early, continuously weighing a range of cues to determine the likely point at which the current turn ends (de Ruiter et al., 2006). Subsequent cross-linguistic work has confirmed this no-gap-no-overlap goal, showing that across 10 languages from 7 language families, floor transfers are usually achieved between 0 and 200ms, with language-specific means falling within 250ms on either side of the mean (Stivers et al., 2009). Currently available data allows us to replicate this in 24 languages from 12 unrelated families, more than doubling the sample size. Because our aim is to characterize the overall temporal features of quotidian interaction, we consider all turn transitions in dyadic stretches of conversation (see Appendix A.1 for a validation in question-answer sequences). In the 24 corpora that contain at least 1000 dyadic turn transitions, we find substantially the 5616 same finely calibrated temporal distribution of turns, suggesting that participants aim for a no-gap, no-overlap target, with the bulk of language-specific means falling within a relatively narrow bandwidth of variation (Figure 4). In the full set of 674 223 transitions, 46% of turns are produced in slight terminal overlap. This includes both fuller turns and short responsive tokens (Goodwin, 1986; Corps et al., 2022), and underlines the extent to which human interaction everywhere involves a braiding of successive and concurrent moves. The implications for language and speech technology are considerable (Skantze, 2021; Roddy, 2021). It means that social robots that switch between listen and talk states will be behind the curve approximately half of the time: perceived as responding too slowly or switching to a listening state too late to pick up early and concurrent responses. If the aim is to facilitate fluid interaction, a first challenge is to achieve the rapid transitions that characterize human language use. This requires incremental and continuous processing (Levinson, 2016; Pitsch, 2016), representing a radical departure from classic reactive spoken dialog systems. Most work in this area is still based on English, potentially jeopardizing the generalizability of findings. Cross-linguistic conversational corpora will prove crucial to identify the most robust prosodic, lexical and interactional features that can inform continuous projections of transition relevance places (Ward et al., 2018; Roddy et al., 2018). Even if rapid transitions may be achieved with the help of continuous, context-sensitive processing, a further layer of language-specific calibration will be necessary to account for the known range of variation (Stivers et al., 2009). Experimental work in this domain shows measurable intercultural differences in orientations to inter-turn silences (Roberts et al., 2011): across cultures, people treat gaps as meaningful beyond a threshold of a few hundred milliseconds, but the exact threshold varies with culture. Without calibration of this kind, people may easily experience conversational agents as overeager, stilted, or out of sync. Progress in this domain may hinge on endowing interactive technologies with a sense for timing and rhythm (Yu et al., 2021; Pouw et al., 2021). An important aspect of human conversation is how rapid turn-taking enables on-the-fly calibration and coordination. This motivates a theoretical turn from singular, perfectly formulated, un-Figure 4: The timing of turn transitions in dyadic interactions in 24 languages around the world, replicating earlier findings and extending the evidence for the interplay of universals and cultural variation in turn-taking ( n = number of turn transitions per corpus). Positive values represent gaps between turns; negative values represent overlaps. Across languages, the mean transition time is 59ms, and 46% of turns are produced in (slight) terminal overlap with a prior turn. ambigous utterances to incremental, good enough, co-constructed understanding (Dingemanse et al., 2015; Albert and de Ruiter, 2018; van Arkel et al., 2020). Increasingly, parsers and other models of grammar and dialogue incorporate this kind of incremental perspective (Schlangen and Skantze, 2011; Vanzo et al., 2018; Buschmeier and Kopp, 2018). Promising application-oriented work in this direction exists (Ekstedt and Skantze, 2020; 5617 Figure 5: Two types of conversational activity in 6 unrelated languages, showing the viability of identifying broad activity types using ebbs and flows in amount of talk contributed (time in ms). Skantze, 2017), though two critical challenges remain: (i) text corpora of asynchronous interaction are much less piecemeal and incremental than copresent interaction, and (ii) the interactional disruptiveness of timing discrepancies can be masked by the flexibility of human participants, who soon learn to revert to simpler forms of robot-directed talk (Suchman, 2007; Seibt, 2017). While text corpora are sometimes treated as shapeless collections of strings, conversational data is not flat but richly structured (Goodwin, 1981; Couper-Kuhlen and Selting, 2017). Each turn at talk builds on what came before and shapes the possibility space of what comes next (Firth, 1935; Heritage, 1984). Conversation analysts call this sequence organization (Schegloff, 2007), and cross-linguistic work has uncovered a number of basic sequential positions along with slots for inserts and expansions (Kendrick et al., 2020). Sequences are one of the major tools for organizing social action. Studying conversational sequences across diverse languages poses considerable challenges, because it requires access not just to form but also to social action or intent (Bender and Koller, 2020). While annotated corpora of dialog acts (Jurafsky et al., 1998) are available for a small number of well-resourced languages (Bunt et al., 2020), they invite an overly categorical view of what is in fact fluid and emergent action ascription. The openendedness of social actions in casual conversation (Levinson, 2013) places severe constraints on the utility of slot-filling approaches (Papaioannou et al., 2018), which have their origin in narrow task-oriented interactions (Liu et al., 2021). Here we probe conversational sequencing by starting from coarse-grained but robust structural facts about the relative distribution of turns and talk. Casual interaction often combine lively spates of equitable exchange with more lopsided moments such as tellings in which one participant secures the floor and the other assumes a recipient role (Schegloff, 1982; Goodwin, 1995). Some work on English has captured this as chat versus chunk , where a chunk is defined as a segment where one speaker takes the floor and is allowed to dominate the conversation for an extended period' (Eggins and Slade, 2004; Gilmartin et al., 2018). Using a measure of relative skew in contributions in a moving 10 second window, we can identify stretches corresponding to such a distinction, as well as transitions from one state to another, across unrelated languages (Figure 5A-C). Knowing about such states and transitions between them is of great relevance to language technology and dialog systems. For instance, the relative predictability of responses differs strongly across states (Gilmartin, 2021). Our results suggest that it is possible to reliably identify at least some broad activity types across languages, opening up possibilities for investigating the linguistic resources that characterize them, and the ways in which people transition between them. The notions of chat' and chunk' should not be reified, but the distinction points to a data-driven way to get ana-5618 Figure 6: A. Continuers (marked  ) are among the most frequent recipient behaviours in tellings (chunks') in both English and Korean, shown here in two 80 second segments each with a strong skew in contributions. lytical grip on structural features of activity types in conversation (Levinson, 1979). Work on English has found that tellings can be recognized not just by their skewed division of labour, but also by the use of continuers like mhm (Howes and Eshghi, 2021; Schegloff, 1982) at places where turn transition would be relevant. We find that this is the case in languages in our sample too, so a simple conclusion could be that we have found a way to unearth universal aspects of tellings, or chunks', with possible implications for the design of, say, dialog systems sensitive to the interactional achievement of dialog states. However, on closer look, the data also provides reason to take linguistic diversity seriously. Figure 6A zooms in on four longer stretches of conversation in English and Korean. Here, circles highlight the use of the most frequent continuer in the language, which is mhm' in English and  eung' in Korean. What is already apparent in the four conversations shown in panel A is also borne out in a quantitative analysis of 100 random samples of 80 second stretches of English and Korean conversations: while 8% of turns are continuers in English, this is 21% in Korean (Figure 6B). This higher frequency also comes with higher susceptibility to overlap: whereas in English, 39% of continuer tokens occurs in full or partial overlap, in Korean this is 73%. The difference does not appear to be reducible to transcription conventions; for instance, in both corpora, continuers repeated in quick succession are transcribed as a distinct format ( mhm mhm ,  eung eung ) and excluded from these counts; and in both corpora, the average number of words per turn lies around 6 (Korean: 5.7; English: 6.9) and the average number of turns per 10 second window is 5.6 (Korean: 5.6; English: 5.6). One implication of this is that continuers are apparently relevant at more points during interaction in Korean than in English (Kim, 1999), which has consequences for the design of dialog systems, incremental parsers and conversational agents. For instance, a conversational agent in Korean might have to issue more displays of recipiency and should be prepared to deal with incoming feedback at a higher pace; in the same context, an agent calibrated to English might need different conversation design. The observed variation is extreme enough to warrant a critical look at the notion of feedback relevance spaces (Howes and Eshghi, 2021): perhaps this notion needs to be relativized to cover attested cross-linguistic diversity, as has been suggested in qualitative conversation analytic research (White, 1989; Clancy et al., 1996; Young and Lee, 2004). We have touched here only on some coarse-grained aspects of sequential structure by way of demonstrating the utility of conversational corpora representing diverse languages. Plenty of other phenomena are ripe for similar treatment. A key finding of linguistics going back to Estoup and Zipf (Estoup, 1917; Zipf, 1935) is that a small number of items tends to be used for a large amount of work. Power law distributions are ubiquitous in linguistic data and well-studied across a range of languages (see Piantadosi 2014 for review). Most analyses in this line of work tokenize textual data on the basis of the observation that sentences are built out of reusable elements. For such tokenised items (roughly, words'), we have come to expect the rank-frequency distribution to look linear on a log/log scale. Yet language does not come in stray words, but in turns at talk: communicative moves 5619 Figure 7: Frequency/rank distributions of tokenized items (words') and recurring turn formats in conversational corpora with at least 20 such turn formats, representing 22 languages (8 phyla). Since communicative turns are rarely studied as holistic units, it is an open question to what extent they may or may not show evidence of linguistic laws. Such an organization may seem prima facie unlikely: after all, we know we build complex turns out of simpler elements like words and phrases, and the unlimited expressive power generated by this compositionality is rightly celebrated as one of the hallmarks of human language (Hockett, 1960). On the other hand, as Firth (1935) noted, Conversa-tion is much more of a roughly prescribed ritual than most people think. Indeed a look at conversational data shows that many turns are not one-offs: at least 28% of the utterances in our sample (436 367 out of 1 532 915 across 63 languages) occur more than once, and over 21% (329 548) occur more than 20 times. Many of these recurring turn formats are interjections and other pragmatic devices that help manage the flow of interaction and calibrate understanding (Yngve, 1970; Jefferson, 1985; Allwood et al., 1990; Ward, 2006; Norrick, 2009). The ubiquity and communicative importance of these items opens up the possibility of power law-like distributions at turn level for some subset of turns. Here we compare rank-frequency distributions of tokenized items and standalone turn formats in the subset of 22 languages with conversational corpora large enough to feature at least 20 recurring standalone turn formats (Figure 7). We find that tokenized items, as expected, reproduce some wellknown structural properties of rank-frequency distributions, including their linear nature on a log-log plot and a systematic deviation from this linearity for the highest frequency (lowest rank) words. For standalone turns, distributions trail off sharply towards the lowest frequencies, reflective of the creative and compositional nature of many utterances. However, the considerable subset of recurring turn formats (Figure 5, purple) may also suggest a partial power law distribution: though the data is sparser, a log-log line fitted to the 20% of turns used at least 20 times has a comparable slope in most corpora. The result cannot simply be reduced to the fact that standalone turns are drawn from the larger population of single words. Recurrent turn formats tend to have specialized discourse-level functions, and while many are single words like m-hm', huh?' or oh', one out of three are multi-word expressions like English but um', Japanese   a soo nanda oh really' or Hungarian nem tudom I dunno'. If such recurring formats obey a power law distribution, this provides novel, interactionally motivated evidence in support of the claim that the phrase rather than the word may be a privileged locus for Zipf's law of frequency (Ryland Williams et al., 2015). In this context it is worth recalling that Zipf motivated his observa-5620 tions in terms of tools-for-jobs (Zipf, 1949). Just as the tools of artisans are constructed and arranged in ways that support efficient use, so the tools of language are organized to optimally carry out their jobs. In this sense, we can speak of recurring turn formats as interactional tools . Even if interactional tools make up a significant proportion of turns in any exchange (as we saw in 4, continuers alone may account for 10 to 20% of turns at talk), they are easily obscured by premature tokenization or erased by seemingly innocuous procedures like stopword removal. And yet it is precisely these interactional tools that may prove essential to understanding and modelling interactional infrastructure within and across languages. Getting at these tools and charting their universality and variability represents a key goal for human language technologies. Overlooking interactional tools and the details of their deployment comes with immediate adverse consequences. A recent user study reported that a significant number of participants ran into interactional turbulence and overlap when interacting with a neural conversational agent through an English-based voice user interface (Hoegen et al., 2019). The turbulence was traced to the agent making segmentation errors and responding to every single utterance detected. This in turn made it harder for human participants to predict when the agent was done speaking, leading to cascades of overlap and confusion. The study proposed two solutions to deal with this (casting the interactional scuffles as situations to be avoided rather than as the rapid and flexible recalibrations they represent in human interaction). The first is to return the floor to a participant as soon as overlap is detected. This seems to assume that any vocalization by a participant is an attempt to take the floor (rather than, say, a minimal display of understanding-so-far). The second proposal is to filter out stop words and interjections from the participant on the grounds that the agent responding to these can confuse participants, since people often do not even realize they are using stop words or are interjecting (p. 117). However, people do not produce interjections stochastically, but wield them as interactional tools in the service of calibrating mutual understanding and coordinating joint action (Dingemanse, 2017). A continuer like mhm shows understanding, while a repair initiator like huh? requests clarification. Indiscriminately filtering out such utterances robs conversational agents of direct access to public displays of understanding and misunderstanding. It also robs people of the very tools they use to co-construct interdependence and understanding, and therefore of a significant part of their linguistic agency. Filtering out interjections to avoid interactional turbulence is like removing all pedestrian crossings to deal with self-driving cars crashing into people. The result may be an incident-free zone, but at significant cost to human flexibility and agency (Illich, 1973). More work is needed to explore the distributional properties of recurring turn formats, but at least we can conclude that every corpus in our dataset has a subset of recurrent turn formats with metacommunicative functions whose organization suggests a power-law distribution. Their importance in human interaction and by extension human-computer interfaces can hardly be overstated. To build flexible conversational agents (Buschmeier and Kopp, 2018) and localizable conversational interfaces (AbuShawar and Atwell, 2016), we need a solid grip both on possibly universal aspects as well as on the full range of cross-linguistic diversity. Recent work has argued that text-based stochastic models may be running into dimishing returns (Bender and Koller, 2020), has stressed the dearth of relevant conversational data (Gilmartin, 2021), and has pointed to formidable challenges in the creation of truly interactive systems (Marge et al., 2022). Progress will come from multiple fronts, but careful and mindful data curation must be a fundamental part of it (Rogers, 2021). This requires a reconceptualization not just of what counts as NLP work, but also of what counts as data. Here we have shown how linguistically diverse corpora of co-present conversation may contribute to such a reconceptualization. Now is the time to pivot from text to talk; for few things other than the careful study of interactive language use can bring us closer to an understanding of how language augments human cognition and supports fluid and flexible action coordination. This understanding, in turn, will be critical to make meaningful progress in any domain that involves human language technologies and interactive interfaces. Fortunately there are good ways forward. Here we summarise three principles to foster a robust and diversity-aware science of human interaction 5621 that can underpin engineering solutions, inform language models, and contribute to human-centered applications: 1. Maximise ecological validity. To understand and model human interaction, start from rich data that is as close as possible to the natural habitat of language: co-present social interaction. Audio and video corpora of informal conversation are increasingly available for many languages and provide an excellent starting point. What such corpora may lack in breadth they make up for in depth: terabytes of text cannot replace the intricacies of multimodal communication and fluid participation. 2. Represent interactional infrastructure. Fine-grained temporal organization, radical interdependency and emergent social action are characteristics of human interaction that cannot be reduced to stochastic properties of text. The timing, co-construction and sequential positioning of turns is as consequential to their meaning and interpretation as their form. The complex and socially distributed nature of sequence organization exceeds the powers of slot filling approaches and requires renewed attention to interactional tools: the metacommunicative resources people use to construct and calibrate mutual understanding on the fly. 3. Design for diversity. To escape the reign of the resourceful few, use linguistically diverse data and anticipate a combination of universal and language-specific design principles. This not only ensures broad empirical coverage and enables new discoveries; it also benefits diversity and inclusion, as it enables language technology development that serves the needs of diverse communities. Our aim in this position paper has been to sketch how these principles, fuelled by insights from the study of dialogue, linguistic typology, conversation analysis, and a range of other fields, can provide the conceptual foundations for novel work on human language technologies and human interaction. Cross-linguistically diverse corpora of conversation are increasingly available and can help us to better understand basic interactional patterns and build more flexible, context-sensitive language technologies. For this to work, it is important to keep both linguistic diversity and potential universals in sight (Sidnell and Enfield, 2012; Enfield et al., 2013). We cannot assume that a given piece of interactional infrastructure is universal just based on a handful of languages. Encouragingly, our results suggest that even relatively small corpora can support robust generalizations about key aspects of interactional infrastructure. One reason this matters is empirical grounding . Cross-linguistic and comparative work on human interaction has barely started (Floyd, 2021; Ameka and Terkourafi, 2019). There may be more universals of interaction; equally likely is that there are more patterns of unrecognized diversity. Both types of outcomes are important for how they shed light on the structure of human interaction, and both have implications for language technology and human-computer interfaces. More fundamental work in pragmatic typology is needed  and computational approaches to low-resource languages provide a promising starting point. But an equally important reason to consider linguistic diversity in language technology and natural language processing is one of linguistic agency (Di Paolo et al., 2018; Nguyen et al., 2016; Such-man, 2020). Designing interfaces that allow people to flexibly wield their preferred communicative resources lessens the hegemony of any one language and makes technology more inclusive, more humane and more convivial for a larger range of possible users (Munn, 2018; Voinea, 2018). Localizing user interface elements is only a first step; diversity in how and when basic interactional structures are deployed must ultimately be reflected in the design of conversational user interfaces. In the rush for better language technology we should avoid being driven into the arms of only the best-resourced languages and the easiest-to-get data. We need language models that are representative of the actual ways in which people use language, and conversational interfaces that give people the feeling they do not have to leave their own linguistic identities at the door. Comparative and computational work on conversational corpora from a wide range of languages is crucial to develop a strong foundational understanding of universals and diversity in interactional infrastructure, and to ensure we can build the humane and diversity-aware language technologies of the future. We thank Calle Brstell, Riccardo Fusaroli, Wim Pouw, Marlou Rasenberg and Marieke Woensdregt for helpful comments. Funding for the work reported here comes from Dutch Research Council grant NWO 016.vidi.185.205 to MD.
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