Coordination in conversation and rapport

Cassell, Justine; Gill, Alastair J.; Tepper, Paul

doi:10.3115/1610065.1610071

Cited by 54 publications

(27 citation statements)

References 20 publications

(21 reference statements)

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“…The above results confirmed our observations and supported the notion that people behave differently according to their relationships with and attitudes toward others [2,10,13,20,40]. Moreover, the results were consistent with the literature suggesting that greater openness and accessibility in communication signifies a closer relationship [2,27].…”

Section: F Summarysupporting

confidence: 91%

Modeling and Controlling Friendliness for An Interactive Museum Robot

Huang

Iio

Satake

et al. 2014

Robotics: Science and Systems X

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Abstract-Advances in robotic technologies have enabled interactive robots to utilize humanlike social behaviors to interact with people in public places such as museums. While these behaviors have shown promise in engaging people, they have been designed and applied to users uniformly. Humans, however, behave differently according to their relationships with others. Behavioral changes, from neutral to friendly, contribute to the development of interpersonal relationships. Friendliness, in particular, plays an important role in the early development of a relationship. In this work, we explore how an interactive robot might nonverbally express a variety of friendly behaviors in a museum scenario. Four behavioral variables-response time, approach speed, individual distance, and attentiveness-contributing to perceived friendliness were modeled and implemented for the interactive museum robot. The results of our study showed that people perceived the differences in the designed robot behaviors and related those differences to the friendliness of the robot to varying degrees. This work serves as a building block toward the development of human-robot relationships and has implications on designing friendly behaviors for interactive robots.

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Section: F Summarysupporting

confidence: 91%

Modeling and Controlling Friendliness for An Interactive Museum Robot

Huang

Iio

Satake

et al. 2014

Robotics: Science and Systems X

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“…Strangers are also much more hesitant to respond to positivity. Compared to friends, we see strangers responding with a much more restricted set of behaviors, suggesting a discomfort with confrontation not seen in friend dyads [24]. When face threat does happen, it is not beneficial for learning.…”

Section: Relationships: Friends and Strangers (Rq3)mentioning

confidence: 74%

Rudeness and Rapport: Insults and Learning Gains in Peer Tutoring

Ogan

Finkelstein

Walker

et al. 2012

Intelligent Tutoring Systems

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Abstract. For 20 years, researchers have envisioned artificially intelligent learning companions that evolve with their students as they grow and learn. However, while communication theory suggests that positivity decreases over time in relationships, most tutoring systems designed to build rapport with a student remain adamantly polite, and may therefore inadvertently distance the learner from the agent over time. We present an analysis of high school friends interacting in a peer tutoring environment as a step towards designing agents that sustain long-term pedagogical relationships with learners. We find that tutees and tutors use different language behaviors: tutees express more playfulness and face-threat, while tutors attend more to the task. This face-threat by the tutee is associated with increased learning gains for their tutor. Additionally, a small sample of partners who were strangers learned less than friends, and in these dyads increased face-threat was negatively correlated with learning. Our findings support the idea that learning companions should gradually move towards playful face-threat as they build relationships with their students.

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“…When faced with such selfdisclosure listeners, on the other hand, often nod and avert their gaze . When the conversational strategy of reference to shared experience is used, speakers are less likely to smile, and more likely to avert their gaze (Cassell et al, 2007). Meanwhile, listeners smile to signal their coordination.…”

Section: Resultsmentioning

confidence: 99%

Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

Katagiri

Nakano

Fernández

et al. 2016

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We extend special thanks to our Local co-Chairs, Ron Artstein and Alesia Gainer, and their team of student volunteers. We know SIGDIAL 2016 would not have been possible without Ron and Alesia, who invested so much effort in arranging the conference venue and accommodations, handling registration, making banquet arrangements, and handling numerous other preparations for the conference. The student volunteers for on-site assistance also deserve our appreciation.Ethan Selfridge, Sponsorships Chair, has earned our appreciation for recruiting and liaising with our conference sponsors, many of whom continue to contribute year after year. Sponsorships support valuable aspects of the program, such as the invited speakers and conference banquet. In recognition of this, we gratefully acknowledge the support of our sponsors: (Platinum level) Microsoft Research, Xerox and PARC, Intel, (Gold level) Facebook, (Silver level) Amazon Alexa, Interactions, Educational Testing Service, Honda Research Institute, and Yahoo!. At the same time, we thank Priscilla Rasmussen at the ACL for tirelessly handling the financial aspects of sponsorship for SIGDIAL 2016, and for securing our ISBN.iii We also thank the SIGdial board, especially officers Amanda Stent, Jason Williams and Kristiina Jokinen for their advice and support from beginning to end.Finally, we thank all the authors of the papers in this volume, and all the conference participants for making this stimulating event a valuable opportunity for growth in the research areas of discourse and dialogue. AbstractThis paper presents an end-to-end framework for task-oriented dialog systems using a variant of Deep Recurrent QNetworks (DRQN). The model is able to interface with a relational database and jointly learn policies for both language understanding and dialog strategy. Moreover, we propose a hybrid algorithm that combines the strength of reinforcement learning and supervised learning to achieve faster learning speed. We evaluated the proposed model on a 20 Question Game conversational game simulator. Results show that the proposed method outperforms the modular-based baseline and learns a distributed representation of the latent dialog state. IntroductionTask-oriented dialog systems have been an important branch of spoken dialog system (SDS) research (Raux et al., 2005; Young, 2006; Bohus and Rudnicky, 2003). The SDS agent has to achieve some predefined targets (e.g. booking a flight) through natural language interaction with the users. The typical structure of a task-oriented dialog system is outlined in Figure 1 (Young, 2006). This pipeline consists of several independently-developed modules: natural language understanding (the NLU) maps the user utterances to some semantic representation. This information is further processed by the dialog state tracker (DST), which accumulates the input of the turn along with the dialog history. The DST outputs the current dialog state and the dialog policy selects the next system action based on the dialog state. Then natural language gene...

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Coordination in conversation and rapport

Cited by 54 publications

References 20 publications

Modeling and Controlling Friendliness for An Interactive Museum Robot

Modeling and Controlling Friendliness for An Interactive Museum Robot

Rudeness and Rapport: Insults and Learning Gains in Peer Tutoring

Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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