Tanmay Sinha
Carnegie Mellon University
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Publication
Featured researches published by Tanmay Sinha.
intelligent virtual agents | 2016
Ran Zhao; Tanmay Sinha; Alan W. Black; Justine Cassell
This work focuses on data-driven discovery of the temporally co-occurring and contingent behavioral patterns that signal high and low interpersonal rapport. We mined a reciprocal peer tutoring corpus reliably annotated for nonverbals like eye gaze and smiles, conversational strategies like self-disclosure and social norm violation, and for rapport (in 30 s thin slices). We then performed a fine-grained investigation of how the temporal profiles of sequences of interlocutor behaviors predict increases and decreases of rapport, and how this rapport management manifests differently in friends and strangers. We validated the discovered behavioral patterns by predicting rapport against our ground truth via a forecasting model involving two-step fusion of learned temporal associated rules. Our framework performs significantly better than a baseline linear regression method that does not encode temporal information among behavioral features. Implications for the understanding of human behavior and social agent design are discussed.
annual meeting of the special interest group on discourse and dialogue | 2016
Ran Zhao; Tanmay Sinha; Alan W. Black; Justine Cassell
In this work, we focus on automatically recognizing social conversational strategies that in human conversation contribute to building, maintaining or sometimes destroying a budding relationship. These conversational strategies include self-disclosure, reference to shared experience, praise and violation of social norms. By including rich contextual features drawn from verbal, visual and vocal modalities of the speaker and interlocutor in the current and previous turn, we can successfully recognize these dialog phenomena with an accuracy of over 80% and kappa ranging from 60-80%. Our findings have been successfully integrated into an end-to-end socially aware dialog system, with implications for virtual agents that can use rapport between user and system to improve task-oriented assistance.
artificial intelligence in education | 2015
Tanmay Sinha; Justine Cassell
Better conversational alignment can lead to shared understanding, changed beliefs, and increased rapport. We investigate the relationship in peer tutoring of convergence, interpersonal rapport, and student learning. We develop an approach for computational modeling of convergence by accounting for the horizontal richness and time-based dependencies that arise in non-stationary and noisy longitudinal interaction streams. Our results, which illustrate that rapport as well as convergence are significantly correlated with learning gains, provide guidelines for development of peer tutoring agents that can increase learning gains through subtle changes to improve tutor-tutee alignment.
Proceedings of the 1st Workshop on Modeling INTERPERsonal SynchrONy And infLuence | 2015
Tanmay Sinha; Ran Zhao; Justine Cassell
Conversational interaction is a dynamic process in which information is conveyed and relationships are negotiated via the use and timing of specific conversational strategies. In this work we examine similarity in use and timing of the relationship-oriented communicative strategies self-disclosure,reference to shared experience and praise, during a reciprocal peer tutoring interaction. We computationally model two kinds of similarity that quantify whether and how students are similar or different in their use of the strategies over time, and differentiate the effects by gender, relationship status and session. In order to assess their impact, we leverage learning and self-reported rapport as outcome variables. Our results show significant effects in cumulative use as well as in the pattern of timings of conversational strategy usage by partners in a dyad, along with interesting relationships to socio-cognitive processes.
european conference on technology enhanced learning | 2017
Tanmay Sinha; Zhen Bai; Justine Cassell
Curiosity is the strong desire to learn or know more about something or someone. Since learning is often a social endeavor, social dynamics in collaborative learning may inevitably influence curiosity. There is a scarcity of research, however, focusing on how curiosity can be evoked in group learning contexts. Inspired by a recently proposed theoretical framework that articulates an integrated socio-cognitive infrastructure of curiosity, in this work, we use data-driven approaches to identify fine-grained social scaffolding of curiosity in child-child interaction, and propose how they can be used to elicit and maintain curiosity in technology-enhanced learning environments. For example, we discovered sequential patterns of multimodal behaviors across group members and we describe those that maximize an individuals utility, or likelihood, of demonstrating curiosity during open-ended problem-solving in group work. We also discovered, and describe here, behaviors that directly or in a mediated manner cause curiosity related conversational behaviors in the interaction, with twice as many interpersonal causal influences compared to intrapersonal ones. We explain how these findings form a solid foundation for developing curiosity-increasing learning technologies or even assisting a human coach to induce curiosity among learners.
european conference on technology enhanced learning | 2017
Tanmay Sinha; Zhen Bai; Justine Cassell
Curiosity is a vital metacognitive skill in educational contexts. Yet, little is known about how social factors influence curiosity in group work. We argue that curiosity is evoked not only through individual, but also interpersonal activities, and present what we believe to be the first theoretical framework that articulates an integrated socio-cognitive account of curiosity based on literature spanning psychology, learning sciences and group dynamics, along with empirical observation of small-group science activity in an informal learning environment. We make a bipartite distinction between individual and interpersonal functions that contribute to curiosity, and multimodal behaviors that fulfill these functions. We validate the proposed framework by leveraging a longitudinal latent variable modeling approach. Findings confirm positive predictive relationship of the latent variables of individual and interpersonal functions on curiosity, with the interpersonal functions exercising a comparatively stronger influence. Prominent behavioral realizations of these functions are also discovered in a data-driven way. This framework can guide the design of learning technologies to recognize and evoke curiosity during learning in social contexts.
Small Group Research | 2017
Roni Reiter-Palmon; Tanmay Sinha; Josette M.P. Gevers; Jean-Marc Odobez; Gualtiero Volpe
This article describes some of the theoretical approaches used by social scientists as well as those used by computer scientists to study the team and group phenomena. The purpose of this article is to identify ways in which these different fields can share and develop theoretical models and theoretical approaches, in an effort to gain a better understanding and further develop team and group research.
empirical methods in natural language processing | 2014
Tanmay Sinha; Patrick Jermann; Nan Li; Pierre Dillenbourg
arXiv: Social and Information Networks | 2014
Tanmay Sinha
learning at scale | 2015
Tanmay Sinha; Justine Cassell