Publications

@inproceedings{manuvinakurike_dialogue_2018,

title = {A Dialogue Annotation Scheme for Weight Management Chat using the Trans-Theoretical Model of Health Behavior Change},

author = {Ramesh Manuvinakurike and Sumanth Bharadwaj and Kallirroi Georgila},

url = {https://arxiv.org/abs/1807.03948},

year  = {2018},

date = {2018-07-01},

booktitle = {Proceedings of the Fourteenth Joint ACL - ISO Workshop on Interoperable Semantic Annotation},

publisher = {arxiv.org},

address = {Sante Fe, New Mexico},

abstract = {A dialogue annotation scheme for weight management chat using the trans-theoretical model of health behavior change},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{de_melo_shaping_2018,

title = {Shaping Cooperation between Humans and Agents with Emotion Expressions and Framing},

author = {Celso M. Melo and Peter Khooshabeh and Ori Amir and Jonathan Gratch},

url = {https://dl.acm.org/citation.cfm?id=3238129},

year  = {2018},

date = {2018-07-01},

booktitle = {Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems},

pages = {2224–2226},

publisher = {International Foundation for Autonomous Agents and Multiagent Systems},

address = {Stockholm, Sweden},

abstract = {Emotion expressions can help solve social dilemmas where individual interest is pitted against the collective interest. Building on research that shows that emotions communicate intentions to others, we reinforce that people can infer whether emotionally expressive computer agents intend to cooperate or compete. We further show important distinctions between computer agents that are perceived to be driven by humans (i.e., avatars) vs. by algorithms (i.e., agents). Our results reveal that, when the emotion expression reflects an intention to cooperate, participants will cooperate more with avatars than with agents; however, when the emotion reflects an intention to compete, participants cooperate just as little with avatars as with agents. Finally, we present first evidence that the way the dilemma is described - or framed - can influence people's decision-making. We discuss implications for the design of autonomous agents that foster cooperation with humans, beyond what game theory predicts in social dilemmas.},

keywords = {ARL, DoD, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{wang_analysis_2018,

title = {An Analysis of Student Belief and Behavior in Learning by Explaining to a Digital Doppelganger},

author = {Ning Wang and Ari Shapiro and Andrew Feng and Cindy Zhuang and David Schwartz and Stephen L. Goldberg},

url = {http://ceur-ws.org/Vol-2141/paper3.pdf},

doi = {10.1007/978-3-319-91464-0_25},

isbn = {978-3-319-91463-3 978-3-319-91464-0},

year  = {2018},

date = {2018-06-01},

booktitle = {Proceedings of the AIED Workshop on Personalized Approaches in Learning Environments (PALE)},

volume = {10858},

pages = {256–264},

publisher = {Springer International Publishing},

address = {Cham, Switzerland},

abstract = {Digital doppelgangers are virtual humans that highly resemble the real self but behave independently. Using a low-cost and high-speed computer graphics and character animation technology, we created digital doppelgangers of students and placed them in a learning-byexplaining task where they interacted with digital doppelgangers of themselves. We investigate the research question of how does increasing the similarity of the physical appearance between the agent and the student impact learning. This paper discusses the design and evaluation of a digital doppelganger as a virtual human listener in a learning-by-explaining paradigm. It presents an analysis of how students’ perceptions of the resemblance impact their learning experience and outcomes. The analysis and results offer insight into the promise and limitation of the application of this novel technology to pedagogical agents research.},

keywords = {ARL, DoD, Social Simulation, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nye_engaging_2018,

title = {Engaging with the Scenario: Affect and Facial Patterns from a Scenario-Based Intelligent Tutoring System},

author = {Benjamin D. Nye and Shamya Karumbaiah and S. Tugba Tokel and Mark G. Core and Giota Stratou and Daniel Auerbach and Kallirroi Georgila},

url = {http://link.springer.com/10.1007/978-3-319-93843-1_26},

doi = {10.1007/978-3-319-93843-1_26},

isbn = {978-3-319-93842-4 978-3-319-93843-1},

year  = {2018},

date = {2018-06-01},

booktitle = {Proceeding of the International Conference on Artificial Intelligence in Education},

volume = {10947},

pages = {352–366},

publisher = {Springer International Publishing},

address = {London, UK},

abstract = {Facial expression trackers output measures for facial action units (AUs), and are increasingly being used in learning technologies. In this paper, we compile patterns of AUs seen in related work as well as use factor analysis to search for categories implicit in our corpus. Although there was some overlap between the factors in our data and previous work, we also identified factors seen in the broader literature but not previously reported in the context of learning environments. In a correlational analysis, we found evidence for relationships between factors and self-reported traits such as academic effort, study habits, and interest in the subject. In addition, we saw differences in average levels of factors between a video watching activity, and a decision making activity. However, in this analysis, we were not able to isolate any facial expressions having a significant positive or negative relationship with either learning gain, or performance once question difficulty and related factors were also considered. Given the overall low levels of facial affect in the corpus, further research will explore different populations and learning tasks to test the possible hypothesis that learners may have been in a pattern of “Over-Flow” in which they were engaged with the system, but not deeply thinking about the content or their errors.},

keywords = {Learning Sciences, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{hampton_mitigating_2018,

title = {Mitigating Knowledge Decay from Instruction with Voluntary Use of an Adaptive Learning System},

author = {Andrew J. Hampton and Benjamin D. Nye and Philip I. Pavlik and William R. Swartout and Arthur C. Graesser and Joseph Gunderson},

url = {http://link.springer.com/10.1007/978-3-319-93846-2_23},

doi = {10.1007/978-3-319-93846-2_23},

isbn = {978-3-319-93845-5 978-3-319-93846-2},

year  = {2018},

date = {2018-06-01},

booktitle = {Proceedings of the International Conference on Artificial Intelligence in Education},

volume = {10948},

pages = {119–133},

publisher = {Springer International Publishing},

address = {London, UK},

abstract = {Knowledge decays across breaks in instruction. Learners lack the metacognition to self-assess their knowledge decay and effectively self-direct review, as well as lacking interactive exercises appropriate to their individual knowledge level. Adaptive learning systems offer the potential to mitigate these issues, by providing open learner models to facilitate learner’s understanding of their knowledge levels and by presenting personalized practice exercises. The current study analyzes differences in knowledge decay between learners randomly assigned to an intervention where they could use an adaptive system during a long gap between courses, compared with a control condition. The experimental condition used the Personal Assistant for Life-Long Learning (PAL3), a tablet-based adaptive learning system integrating multiple intelligent tutoring systems and conventional learning resources. It contained electronics content relevant to the experiment participants, Navy sailors who graduated from apprentice electronics courses (A-School) awaiting assignment to their next training (C-School). The study was conducted over one month, collecting performance data with a counterbalanced pre-, mid-, and post-test. The control condition exhibited the expected decay. The PAL3 condition showed a significant difference from the control, with no significant knowledge decay in their overall knowledge, despite substantial variance in usage for PAL3 (e.g., most of overall use in the first week, with fewer participants engaging as time went on). Interestingly, while overall decay was mitigated in PAL3, this result was primarily through gains in some knowledge offsetting losses in other knowledge. Overall, these results indicate that adaptive study tools can help prevent knowledge decay, even with voluntary usage.},

keywords = {Learning Sciences, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{bonial_human-robot_2018,

title = {Human-Robot Dialogue and Collaboration in Search and Navigation},

author = {Claire Bonial and Stephanie M. Lukin and Ashley Foots and Cassidy Henry and Matthew Marge and Kimberly A. Pollard and Ron Artstein and David Traum and Clare R. Voss},

url = {http://www.areaworkshop.org/wp-content/uploads/2018/05/4.pdf},

year  = {2018},

date = {2018-05-01},

booktitle = {Proceedings of the AREA Workshop: Annotation, Recognition, and Evaluation of Actions},

publisher = {AREA 2018},

address = {Miyazaki, Japan},

abstract = {Collaboration with a remotely located robot in tasks such as disaster relief and search and rescue can be facilitated by grounding natural language task instructions into actions executable by the robot in its current physical context. The corpus we describe here provides insight into the translation and interpretation a natural language instruction undergoes starting from verbal human intent, to understanding and processing, and ultimately, to robot execution. We use a ‘Wizard-of-Oz’ methodology to elicit the corpus data in which a participant speaks freely to instruct a robot on what to do and where to move through a remote environment to accomplish collaborativesearchandnavigationtasks. Thisdataoffersthepotentialforexploringandevaluatingactionmodelsbyconnectingnatural language instructions to execution by a physical robot (controlled by a human ‘wizard’). In this paper, a description of the corpus (soon to be openly available) and examples of actions in the dialogue are provided.},

keywords = {ARL, DoD, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{brixey_chahta_2018,

title = {Chahta Anumpa: A Multimodal Corpus of the Choctaw Language},

author = {Jacqueline Brixey and Eli Pincus and Ron Artstein},

url = {http://www.lrec-conf.org/proceedings/lrec2018/summaries/822.html},

year  = {2018},

date = {2018-05-01},

booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},

pages = {3371–3376},

publisher = {ELRA},

address = {Miyazaki, Japan},

abstract = {This paper presents a general use corpus for the Native American indigenous language Choctaw. The corpus contains audio, video, and text resources, with many texts also translated in English. The Oklahoma Choctaw and the Mississippi Choctaw variants of the language are represented in the corpus. The data set provides documentation support for the threatened language, and allows researchers and language teachers access to a diverse collection of resources.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{traum_dialogue_2018,

title = {Dialogue Structure Annotation for Multi-Floor Interaction},

author = {David Traum and Cassidy Henry and Stephanie Lukin and Ron Artstein and Felix Gervitz and Kim Pollard and Claire Bonial and Su Lei and Clare R. Voss and Matthew Marge and Cory J. Hayes and Susan G. Hill},

url = {http://www.lrec-conf.org/proceedings/lrec2018/summaries/672.html},

isbn = {979-10-95546-00-9},

year  = {2018},

date = {2018-05-01},

booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},

pages = {104–111},

publisher = {ELRA},

address = {Miyazaki, Japan},

abstract = {We present an annotation scheme for meso-level dialogue structure, specifically designed for multi-floor dialogue. The scheme includes a transaction unit that clusters utterances from multiple participants and floors into units according to realization of an initiator’s intent, and relations between individual utterances within the unit. We apply this scheme to annotate a corpus of multi-floor human-robot interaction dialogues. We examine the patterns of structure observed in these dialogues and present inter-annotator statistics and relative frequencies of types of relations and transaction units. Finally, some example applications of these annotations are introduced.},

keywords = {ARL, DoD, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{manuvinakurike_edit_2018,

title = {Edit me: A Corpus and a Framework for Understanding Natural Language Image Editing},

author = {Ramesh Manuvinakurike and Jacqueline Brixey and Trung Bui and Walter Chang and Doo Soon Kim and Ron Artstein and Kallirroi Georgila},

url = {http://www.lrec-conf.org/proceedings/lrec2018/pdf/481.pdf},

year  = {2018},

date = {2018-05-01},

booktitle = {Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC)},

publisher = {LREC},

address = {Miyazaki, Japan},

abstract = {This paper introduces the task of interacting with an image editing program through natural language. We present a corpus of image edit requests which were elicited for real world images, and an annotation framework for understanding such natural language instructions and mapping them to actionable computer commands. Finally, we evaluate crowd-sourced annotation as a means of efficiently creating a sizable corpus at a reasonable cost.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{stocco_empirical_2018,

title = {Empirical Evidence from Neuroimaging Data for a Standard Model of the Mind},

author = {Andrea Stocco and John Laird and Christian Lebiere and Paul Rosenbloom},

url = {https://www.researchgate.net/publication/325106544_Empirical_Evidence_from_Neuroimaging_Data_for_a_Standard_Model_of_the_Mind},

year  = {2018},

date = {2018-05-01},

booktitle = {Proceedings of the 40th Annual Meeting of the Cognitive Science Society},

publisher = {Cognitive Science Society},

address = {Madison, WI},

abstract = {In a recent paper, Laird, Lebiere, and Rosenbloom (2017) highlight how 40 years of research on cognitive architectures has begun to yield a dramatic convergence of different approaches towards a set of basic assumptions that they called the “Standard Model of the Mind” (SMM), in analogy to the Standard Model of particle physics. The SMM was designed to capture a consensus view of “human-like minds”, whether from AI or cognitive science, which if valid must also be true of the human brain. Here, we provide a preliminary test of this hypothesis based on a re-analysis of fMRI data from four tasks that span a wide range of cognitive functions and cognitive complexity, and are representative of the specific form of intelligence and flexibility that is associated with higherlevel human cognition. Using an established method (Dynamic Causal Modeling) to examine functional connectivity between brain regions, the SMM was compared against two alternative models that violate either functional or structural assumptions of the SMM. The results show that, in every dataset, the SMM significantly outperforms the other models, suggesting that the SMM best captures the functional requirements of brain dynamics in fMRI data among these alternatives.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{georgila_toward_2018,

title = {Toward Low-Cost Automated Evaluation Metrics for Internet of Things Dialogues},

author = {Kallirroi Georgila and Carla Gordon and Hyungtak Choi and Jill Boberg and Heesik Jeon and David Traum},

url = {http://www.colips.org/conferences/iwsds2018/wp/wp-content/uploads/2018/03/IWSDS-2018_paper_18.pdf},

year  = {2018},

date = {2018-05-01},

booktitle = {Proceedings of the 9th International Workshop on Spoken Dialogue Systems Technology (IWSDS)},

publisher = {IWSDS},

address = {Singapore},

abstract = {We analyze a corpus of system-user dialogues in the Internet of Things domain. Our corpus is automatically, semi-automatically, and manually annotated with a variety of features both on the utterance level and the full dialogue level. The corpus also includes human ratings of dialogue quality collected via crowdsourcing. We calculate correlations between features and human ratings to identify which features are highly associated with human perceptions about dialogue quality in this domain. We also perform linear regression and derive a variety of dialogue quality evaluation functions. These evaluation functions are then applied to a heldout portion of our corpus, and are shown to be highly predictive of human ratings and outperform standard reward-based evaluation functions.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{xiao_comparison_2018,

title = {A Comparison of Reinforcement Learning Methodologies in Two-Party and Three-Party Negotiation Dialogue},

author = {Gang Xiao and Kallirroi Georgila},

url = {https://aaai.org/ocs/index.php/FLAIRS/FLAIRS18/paper/view/17687},

year  = {2018},

date = {2018-05-01},

booktitle = {Proceedings of the The Thirty-First International Florida Artificial Intelligence Research Society Conference (FLAIRS-31)},

publisher = {AAAI},

address = {Melbourne, FL},

abstract = {We use reinforcement learning to learn dialogue policies in a collaborative furniture layout negotiation task. We employ a variety of methodologies (i.e., learning against a simulated user versus co-learning) and algorithms. Our policies achieve the best solution or a good solution to this problem for a variety of settings and initial conditions, including in the presence of noise (e.g., due to speech recognition or natural language understanding errors). Also, our policies perform well even in situations not observed during training. Policies trained against a simulated user perform well while interacting with policies trained through co-learning, and vice versa. Furthermore, policies trained in a two-party setting are successfully applied to a three-party setting, and vice versa.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{artstein_niki_2018,

title = {The Niki and Julie Corpus: Collaborative Multimodal Dialogues between Humans, Robots, and Virtual Agents},

author = {Ron Artstein and Jill Boberg and Alesia Gainer and Jonathan Gratch and Emmanuel Johnson and Anton Leuski and Gale Lucas and David Traum},

url = {http://www.lrec-conf.org/proceedings/lrec2018/pdf/482.pdf},

isbn = {979-10-95546-00-9},

year  = {2018},

date = {2018-05-01},

booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},

publisher = {European Language Resources Association (ELRA)},

address = {Miyazaki, Japan},

abstract = {The Niki and Julie corpus contains more than 600 dialogues between human participants and a human-controlled robot or virtual agent, engaged in a series of collaborative item-ranking tasks designed to measure influence. Some of the dialogues contain deliberate conversational errors by the robot, designed to simulate the kinds of conversational breakdown that are typical of present-day automated agents. Data collected include audio and video recordings, the results of the ranking tasks, and questionnaire responses; some of the recordings have been transcribed and annotated for verbal and nonverbal feedback. The corpus has been used to study influence and grounding in dialogue. All the dialogues are in American English.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{vinkemeier_predicting_2018,

title = {Predicting Folds in Poker Using Action Unit Detectors and Decision Trees},

author = {Doratha Vinkemeier and Michel Valstar and Jonathan Gratch},

url = {https://ieeexplore.ieee.org/document/8373874/},

doi = {10.1109/FG.2018.00081},

isbn = {978-1-5386-2335-0},

year  = {2018},

date = {2018-05-01},

booktitle = {Proceedings of the 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)},

pages = {504–511},

publisher = {IEEE},

address = {Xi'an, China},

abstract = {Predicting how a person will respond can be very useful, for instance when designing a strategy for negotiations. We investigate whether it is possible for machine learning and computer vision techniques to recognize a person's intentions and predict their actions based on their visually expressive behaviour, where in this paper we focus on the face. We have chosen as our setting pairs of humans playing a simplified version of poker, where the players are behaving naturally and spontaneously, albeit mediated through a computer connection. In particular, we ask if we can automatically predict whether a player is going to fold or not. We also try to answer the question of at what time point the signal for predicting if a player will fold is strongest. We use state-of-the-art FACS Action Unit detectors to automatically annotate the players facial expressions, which have been recorded on video. In addition, we use timestamps of when the player received their card and when they placed their bets, as well as the amounts they bet. Thus, the system is fully automated. We are able to predict whether a person will fold or not significantly better than chance based solely on their expressive behaviour starting three seconds before they fold.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{wauck_analyzing_2018,

title = {Analyzing the Effect of Avatar Self-Similarity on Men and Women in a Search and Rescue Game},

author = {Helen Wauck and Gale Lucas and Ari Shapiro and Andrew Feng and Jill Boberg and Jonathan Gratch},

url = {http://dl.acm.org/citation.cfm?doid=3173574.3174059},

doi = {10.1145/3173574.3174059},

isbn = {978-1-4503-5620-6},

year  = {2018},

date = {2018-04-01},

booktitle = {Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems},

pages = {1–12},

publisher = {ACM Press},

address = {Montreal, Canada},

abstract = {A crucial aspect of virtual gaming experiences is the avatar: the player's virtual self-representation. While research has demonstrated benefits to using self-similar avatars in some virtual experiences, such avatars sometimes produce a more negative experience for women. To help researchers and game designers assess the cost-benefit tradeoffs of self-similar avatars, we compared players' performance and subjective experience in a search and rescue computer game when using two different photorealistic avatars: their own self or a friend, and when playing either a social (rescuing people) or a nonsocial (rescuing gems) version of the game. There was no effect of avatar appearance on players' performance or subjective experience in either game version, but we also found that women's experience with self-similar avatars was no more negative than men's. Our results suggest that avatar appearance may not make a difference to players in certain game contexts.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{pincus_importance_2018,

title = {The Importance of Regulatory Fit & Early Success in a Human-Machine Game},

author = {Eli Pincus and Su Lei and Gale Lucas and Emmanuel Johnson and Michael Tsang and Jonathan Gratch and David Traum},

url = {http://dl.acm.org/citation.cfm?doid=3183654.3183661},

doi = {10.1145/3183654.3183661},

isbn = {978-1-4503-5420-2},

year  = {2018},

date = {2018-04-01},

booktitle = {Proceedings of the first APA ACM Technology, Mind and Society Conference},

pages = {1–6},

publisher = {ACM Press},

address = {Washington D.C.},

abstract = {In this paper, we explore the potential of regulatory focus theory as a framework for personalizing human-machine interactions. We manipulate framing (gain or loss) of a collaborative word-guessing game where a fully-automated virtual human gives clues. Consistent with previous work on regulatory focus, we find evidence of significantly higher perceived task-success when participants have regulatory fit. Inconsistent with previous work, however, fit did not increase task-enjoyment (nor performance). Participants with gain framing had marginally higher enjoyment, regardless of their regulatory focus. We operationalize motivation by number of optional rounds played but failed to find a "fit" effect. Instead, players who achieved early success (scoring more points in initial rounds) were more motivated. Early success was significantly correlated with number of optional rounds played. This finding calls to attention the need for the literature to more thoroughly investigate the relationship between success-timing and total player playtime in the game.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{scassellati_teaching_2018,

title = {Teaching Language to Deaf Infants with a Robot and a Virtual Human},

author = {Brian Scassellati and Ari Shapiro and David Traum and Laura-Ann Petitto and Jake Brawer and Katherine Tsui and Setareh Nasihati Gilani and Melissa Malzkuhn and Barbara Manini and Adam Stone and Geo Kartheiser and Arcangelo Merla},

url = {http://dl.acm.org/citation.cfm?doid=3173574.3174127},

doi = {10.1145/3173574.3174127},

isbn = {978-1-4503-5620-6},

year  = {2018},

date = {2018-04-01},

booktitle = {Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems},

pages = {1–13},

publisher = {ACM Press},

address = {Montreal, Canada},

abstract = {Children with insufficient exposure to language during critical developmental periods in infancy are at risk for cognitive, language, and social deficits [55]. This is especially difficult for deaf infants, as more than 90% are born to hearing parents with little sign language experience [48]. We created an integrated multi-agent system involving a robot and virtual human designed to augment language exposure for 6-12 month old infants. Human-machine design for infants is challenging, as most screen-based media are unlikely to support learning in [33]. While presently, robots are incapable of the dexterity and expressiveness required for signing, even if it existed, developmental questions remain about the capacity for language from artificial agents to engage infants. Here we engineered the robot and avatar to provide visual language to effect socially contingent human conversational exchange. We demonstrate the successful engagement of our technology through case studies of deaf and hearing infants.},

keywords = {Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{lucas_getting_2018,

title = {Getting to Know Each Other: The Role of Social Dialogue in Recovery from Errors in Social Robots},

author = {Gale M. Lucas and Jill Boberg and David Traum and Ron Artstein and Jonathan Gratch and Alesia Gainer and Emmanuel Johnson and Anton Leuski and Mikio Nakano},

url = {http://dl.acm.org/citation.cfm?doid=3171221.3171258},

doi = {10.1145/3171221.3171258},

isbn = {978-1-4503-4953-6},

year  = {2018},

date = {2018-03-01},

booktitle = {Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction},

pages = {344–351},

publisher = {ACM Press},

address = {Chicago, IL},

abstract = {This work explores the extent to which social dialogue can mitigate (or exacerbate) the loss of trust caused when robots make conversational errors. Our study uses a NAO robot programmed to persuade users to agree with its rankings on two tasks. We perform two manipulations: (1) The timing of conversational errors - the robot exhibited errors either in the first task, the second task, or neither; (2) The presence of social dialogue - between the two tasks, users either engaged in a social dialogue with the robot or completed a control task. We found that the timing of the errors matters: replicating previous research, conversational errors reduce the robot's influence in the second task, but not on the first task. Social dialogue interacts with the timing of errors, acting as an intensifier: social dialogue helps the robot recover from prior errors, and actually boosts subsequent influence; but social dialogue backfires if it is followed by errors, because it extends the period of good performance, creating a stronger contrast effect with the subsequent errors. The design of social robots should therefore be more careful to avoid errors after periods of good performance than early on in a dialogue.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{rosenbloom_lessons_2017,

title = {Lessons from Mapping Sigma onto the Standard Model of the Mind: Self-Monitoring, Memory/Learning, and Symbols},

author = {Paul S. Rosenbloom},

url = {https://aaai.org/ocs/index.php/FSS/FSS17/paper/view/15947},

year  = {2017},

date = {2017-11-01},

booktitle = {Proceedings of the AAAI 2017 Fall Symposium on A Standard Model of the Mind},

publisher = {AAAI},

address = {Arlington, VA},

abstract = {Sigma was one of the three architectures explicitly factored into the recent development of the standard model of the mind. Here we dig deeper into the mapping of Sigma onto the standard model begun there to explore three lessons that illustrate outstanding “issues” with the current standard model while providing food for thought for its future development.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{neubauer_relationship_2017,

title = {The relationship between task-induced stress, vocal changes, and physiological state during a dyadic team task},

author = {Catherine Neubauer and Mathieu Chollet and Sharon Mozgai and Mark Dennison and Peter Khooshabeh and Stefan Scherer},

url = {http://dl.acm.org/citation.cfm?doid=3136755.3136804},

doi = {10.1145/3136755.3136804},

isbn = {978-1-4503-5543-8},

year  = {2017},

date = {2017-11-01},

booktitle = {Proceedings of the 19th ACM International Conference on Multimodal Interaction},

pages = {426–432},

publisher = {ACM Press},

address = {Glasgow, UK},

abstract = {It is commonly known that a relationship exists between the human voice and various emotional states. Past studies have demonstrated changes in a number of vocal features, such as fundamental frequency f0 and peakSlope, as a result of varying emotional state. These voice characteristics have been shown to relate to emotional load, vocal tension, and, in particular, stress. Although much research exists in the domain of voice analysis, few studies have assessed the relationship between stress and changes in the voice during a dyadic team interaction. The aim of the present study was to investigate the multimodal interplay between speech and physiology during a high-workload, high-stress team task. Specifically, we studied task-induced effects on participants' vocal signals, specifically, the f0 and peakSlope features, as well as participants' physiology, through cardiovascular measures. Further, we assessed the relationship between physiological states related to stress and changes in the speaker's voice. We recruited participants with the specific goal of working together to diffuse a simulated bomb. Half of our sample participated in an "Ice Breaker" scenario, during which they were allowed to converse and familiarize themselves with their teammate prior to the task, while the other half of the sample served as our "Control". Fundamental frequency (f0), peakSlope, physiological state, and subjective stress were measured during the task. Results indicated that f0 and peakSlope significantly increased from the beginning to the end of each task trial, and were highest in the last trial, which indicates an increase in emotional load and vocal tension. Finally, cardiovascular measures of stress indicated that the vocal and emotional load of speakers towards the end of the task mirrored a physiological state of psychological "threat".},

keywords = {ARL, DoD, UARC, VHTL, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{lucas_role_2017,

title = {The Role of Social Dialogue and Errors in Robots},

author = {Gale M. Lucas and Jill Boberg and David Traum and Ron Artstein and Jon Gratch and Alesia Gainer and Emmanuel Johnson and Anton Leuski and Mikio Nakano},

url = {http://dl.acm.org/citation.cfm?doid=3125739.3132617},

doi = {10.1145/3125739.3132617},

isbn = {978-1-4503-5113-3},

year  = {2017},

date = {2017-10-01},

booktitle = {Proceedings of the 5th International Conference on Human Agent Interaction},

pages = {431–433},

publisher = {ACM Press},

address = {Bielefeld, Germany},

abstract = {Social robots establish rapport with human users. This work explores the extent to which rapport-building can benefit (or harm) conversations with robots, and under what circumstances this occurs. For example, previous work has shown that agents that make conversational errors are less capable of influencing people than agents that do not make errors [1]. Some work has shown this effect with robots, but prior research has not considered additional factors such as the level of rapport between the person and the robot. We predicted that building rapport through a social dialogue (such as an ice-breaker) could mitigate the detrimental effect of a robot's errors on influence. Our study used a Nao robot programmed to persuade users to agree with its rankings on two "survival tasks" (e.g., lunar survival task). We manipulated both errors and social dialogue:the robot either exhibited errors in the second survival task or not, and users either engaged in an ice-breaker with the robot between the two survival tasks or completed a control task. Replicating previous research, errors tended to reduce the robot's influence in the second survival task. Contrary to our prediction, results revealed that the ice-breaker did not mitigate the effect of errors, and if anything, errors were more harmful after the ice-breaker (intended to build rapport) than in the control condition. This backfiring of attempted rapport-building may be due to a contrast effect, suggesting that the design of social robots should avoid introducing dialogues of incongruent quality.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{stratou_investigating_2017,

title = {Investigating Gender Differences in Temporal Dynamics during an Iterated Social Dilemma: an Automatic Analysis Using Networks},

author = {Giota Stratou and Rens Hoegen and Gale Lucas and Jonathan Gratch},

url = {http://ict.usc.edu/pubs/Investigating%20Gender%20Differences%20in%20Temporal%20Dynamics%20during%20an%20Iterated%20Social.pdf},

year  = {2017},

date = {2017-10-01},

booktitle = {Proceedings of the 7th International Conference on Affective Computing and Intelligent Interaction},

address = {San Antonio, TX},

abstract = {Emotions have a temporal nature and very often personality traits and underlying psychological conditions are hidden in the dynamics of those expressions. Within this work, we investigate the dynamics of the facial displays of dyads during an iterated social dilemma. We focus on the effect of gender and gender-pairing on those behaviors. We use networks to capture the temporal dynamics and create measures of inter- and intra- personal dependencies of emotional states. Our analysis on an iterated prisoner’s dilemma corpus suggests that there are gender differences on the transitions of the emotional states and the degree of emotional influence from the opponent.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{stratou_refactoring_2017,

title = {Refactoring Facial Expressions: an Automatic Analysis of Natural Occurring Facial Expressions in Iterative Social Dilemma},

author = {Giota Stratou and Job Van Der Schalk and Rens Hoegen and Jonathan Gratch},

url = {http://ict.usc.edu/pubs/Refactoring%20Facial%20Expressions-an%20Automatic%20Analysis%20of%20Natural%20Occurring%20Facial.pdf},

year  = {2017},

date = {2017-10-01},

booktitle = {Proceedings of the 7th International Conference on Affective Computing and Intelligent Interaction},

address = {San Antonio, TX},

abstract = {Many automatic facial expression recognizers now output individual facial action units (AUs), but several lines of evidence suggest that it is the combination of AUs that is psychologically meaningful: e.g., (a) constraints arising from facial morphology, (b) prior published evidence, (c) claims arising from basic emotion theory. We performed factor analysis on a large data set and recovered factors that have been discussed in the literature as psychologically meaningful. Further we show that some of these factors have external validity in that they predict participant behaviors in an iterated prisoner’s dilemma task and in fact with more precision than the individual AUs. These results both reinforce the validity of automatic recognition (as these factors would be expected from accurate AU detection) and suggest the benefits of using such factors for understanding these facial expressions as social signals.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{narang_generating_2017,

title = {Generating Virtual Avatars with Personalized Walking Gaits using Commodity Hardware},

author = {Sahil Narang and Andrew Best and Ari Shapiro and Dinesh Manocha},

url = {http://dl.acm.org/citation.cfm?doid=3126686.3126766},

doi = {10.1145/3126686.3126766},

isbn = {978-1-4503-5416-5},

year  = {2017},

date = {2017-10-01},

booktitle = {Proceedings of the on Thematic Workshops of ACM Multimedia 2017 - Thematic Workshops '17},

pages = {219–227},

publisher = {ACM Press},

address = {Mountain View, California, USA},

abstract = {We present a novel algorithm for generating virtual avatars which move like the represented human subject, using inexpensive sensors & commodity hardware. Our algorithm is based on a perceptual study that evaluates self-recognition and similarity of gaits rendered on virtual avatars. We identify discriminatory features of human gait and propose a data-driven synthesis algorithm that can generate a set of similar gaits from a single walker. These features are combined to automatically synthesize personalized gaits for a human user from noisy motion capture data. The overall approach is robust and can generate new gaits with little or no artistic intervention using commodity sensors in a simple laboratory setting. We demonstrate our approach's application in rapidly animating virtual avatars of new users with personalized gaits, as well as procedurally generating distinct but similar "families" of gait in virtual environments.},

keywords = {Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{tsang_comparing_2017,

title = {Comparing models for gesture recognition of children's bullying behaviors},

author = {Michael Tsang and Vadim Korolik and Stefan Scherer and Maja Matarić},

url = {https://ieeexplore.ieee.org/abstract/document/8273591/},

doi = {10.1109/ACII.2017.8273591},

year  = {2017},

date = {2017-10-01},

booktitle = {Affective Computing and Intelligent Interaction (ACII), 2017 Seventh International Conference on},

pages = {138–145},

publisher = {IEEE},

address = {San Antonio, TX},

abstract = {We explored gesture recognition applied to the problem of classifying natural physical bullying behaviors by children. To capture natural bullying behavior data, we developed a humanoid robot that used hand-coded gesture recognition to identify basic physical bullying gestures and responded by explaining why the gestures were inappropriate. Children interacted with the robot by trying various bullying behaviors, thereby allowing us to collect a natural bullying behavior dataset for training the classifiers. We trained three different sequence classifiers using the collected data and compared their effectiveness at classifying different types of common physical bullying behaviors. Overall, Hidden Conditional Random Fields achieved the highest average F1 score (0.645) over all tested gesture classes.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ringeval_summary_2017,

title = {Summary for AVEC 2017: Real-life Depression and Affect Challenge and Workshop},

author = {Fabien Ringeval and Björn Schuller and Michel Valstar and Jonathan Gratch and Roddy Cowie and Maja Pantic},

url = {http://dl.acm.org/citation.cfm?doid=3123266.3132049},

doi = {10.1145/3123266.3132049},

isbn = {978-1-4503-4906-2},

year  = {2017},

date = {2017-10-01},

booktitle = {Proceedings of the 2017 ACM on Multimedia Conference},

pages = {1963–1964},

publisher = {ACM Press},

address = {Mountain View, CA},

abstract = {The seventh Audio-Visual Emotion Challenge and workshop AVEC 2017 was held in conjunction with ACM Multimedia'17. This year, the AVEC series addresses two distinct sub-challenges: emotion recognition and depression detection. The Affect Sub-Challenge is based on a novel dataset of human-human interactions recorded 'in-the-wild', whereas the Depression Sub-Challenge is based on the same dataset as the one used in AVEC 2016, with human-agent interactions. In this summary, we mainly describe participation and its conditions.},

keywords = {UARC, VHTL, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{pincus_incremental_2017,

title = {An Incremental Response Policy in an Automatic Word-Game},

author = {Eli Pincus and David Traum},

url = {http://people.ict.usc.edu/ traum/Papers/pincus_traum-cihai2017.pdf},

year  = {2017},

date = {2017-08-01},

booktitle = {Proceedings of IVA 2017 Workshop on Conversational Interruptions in Human-Agent Interactions},

address = {Stockholm, Sweden},

abstract = {Turn-taking is an important aspect of human-human and human-computer interaction. Rapid turn-taking is a feature of human-human interaction that is difficult for today’s dialogue systems to emulate. For example, typical humanhuman interactions can involve an original sending interlocutor changing or stopping their speech mid-utterance as a result of overlapping speech from the other interlocutor. The overlapping utterances from the other interlocutor are typically called barge-in utterances. An example of this phenomena is seen in the two turns of dialogue in the top half of Figure 1. In this dialogue segment Student A first reveals his test score in the original utterance. Student A then begins to tell student B that he had heard Student B got a perfect score. Student B interrupts Student A with a barge-in utterance that contains new information (that actually he had not performed well on the test) causing Student A to halt his speech and not finish his original utterance. We call the unspoken part of student A’s original utterance Student A’s originally intended utterance. Student A then makes a decision based on the new information to not say his originally intended utterance. This is likely due to the originally intended utterance no longer being appropriate considering the new information made available to Student A. Student A then makes an intelligent next choice of what to say which can be seen in Student A’s updated utterance which takes into account the new information contained in Student B’s barge-in utterance. In this work we refer to Student A’s dialogue move as an intelligent update.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{morales_openmm_2017,

title = {OpenMM: An Open-Source Multimodal Feature Extraction Tool},

author = {Michelle Renee Morales and Stefan Scherer and Rivka Levitan},

url = {https://www.researchgate.net/publication/319185055_OpenMM_An_Open-Source_Multimodal_Feature_Extraction_Tool},

doi = {10.21437/Interspeech.2017-1382},

year  = {2017},

date = {2017-08-01},

booktitle = {Proceedings of Interspeech 2017},

pages = {3354–3358},

publisher = {ISCA},

address = {Stockholm, Sweden},

abstract = {The primary use of speech is in face-to-face interactions andsituational context and human behavior therefore intrinsicallyshape and affect communication. In order to usefully modelsituational awareness, machines must have access to the samestreams of information humans have access to. In other words,we need to provide machines with features that represent eachcommunicative modality: face and gesture, voice and speech,and language. This paper presents OpenMM: an open-sourcemultimodal feature extraction tool. We build upon existingopen-source repositories to present the first publicly availabletool for multimodal feature extraction. The tool provides apipeline for researchers to easily extract visual and acousticfeatures. In addition, the tool also performs automatic speechrecognition (ASR) and then uses the transcripts to extract lin-guistic features. We evaluate the OpenMM’s multimodal fea-ture set on deception, depression and sentiment classificationtasks and show its performance is very promising. This tool pro-vides researchers with a simple way of extracting multimodalfeatures and consequently a richer and more robust feature rep-resentation for machine learning tasks. OpenMM: An Open-Source Multimodal Feature Extraction Tool (PDF Download Available). Available from: https://www.researchgate.net/publication/319185055_OpenMM_An_Open-Source_Multimodal_Feature_Extraction_Tool [accessed Sep 13, 2017].},

keywords = {Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{kennedy_learning_2017,

title = {Learning and Reusing Dialog for Repeated Interactions with a Situated Social Agent},

author = {James Kennedy and Iolanda Leite and André Pereira and Ming Sun and Boyang Li and Rishub Jain and Ricson Cheng and Eli Pincus and Elizabeth J. Carter and Jill Fain Lehman},

url = {http://link.springer.com/10.1007/978-3-319-67401-8_22},

doi = {10.1007/978-3-319-67401-8_22},

isbn = {978-3-319-67400-1 978-3-319-67401-8},

year  = {2017},

date = {2017-08-01},

booktitle = {Proceedings of the International Conference on Intelligent Virtual Agents},

volume = {10498},

pages = {192–204},

publisher = {Springer International Publishing},

address = {Stockholm, Sweden},

abstract = {Content authoring for conversations is a limiting factor in creating verbal interactions with intelligent virtual agents. Building on techniques utilizing semi-situated learning in an incremental crowdworking pipeline, this paper introduces an embodied agent that self-authors its own dialog for social chat. In particular, the autonomous use of crowdworkers is supplemented with a generalization method that borrows and assesses the validity of dialog across conversational states. We argue that the approach offers a community-focused tailoring of dialog responses that is not available in approaches that rely solely on statistical methods across big data. We demonstrate the advantages that this can bring to interactions through data collected from 486 conversations between a situated social agent and 22 users during a 3 week long evaluation period.},

keywords = {Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{chollet_racing_2017,

title = {Racing Heart and Sweaty Palms What Influences Users’ Self-Assessments and Physiological Signals When Interacting With Virtual Audiences?},

author = {Mathieu Chollet and Talie Massachi and Stefan Scherer},

url = {http://link.springer.com/10.1007/978-3-319-67401-8_9},

doi = {10.1007/978-3-319-67401-8_9},

isbn = {978-3-319-67400-1 978-3-319-67401-8},

year  = {2017},

date = {2017-08-01},

booktitle = {Proceedings of the International Conference on Intelligent Virtual Agents},

volume = {10498},

pages = {83–86},

publisher = {Springer International Publishing},

address = {Stockholm, Sweden},

abstract = {In psychotherapy, virtual audiences have been shown to promote successful outcomes when used to help treating public speaking anxiety. Additionally, early experiments have shown its potential to help improve public speaking ability. However, it is still unclear to what extent certain factors, such as audience non-verbal behaviors, impact users when interacting with a virtual audience. In this paper, we design an experimental study to investigate users’ self-assessments and physiological states when interacting with a virtual audience. Our results showed that virtual audience behaviors did not influence participants self-assessments or physiological responses, which were instead predominantly determined by participants’ prior anxiety levels.},

keywords = {Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{morales_cross-modal_2017,

title = {A Cross-modal Review of Indicators for Depression Detection Systems},

author = {Michelle Renee Morales and Stefan Scherer and Rivka Levitan},

url = {http://aclweb.org/anthology/W17-3101},

year  = {2017},

date = {2017-08-01},

booktitle = {Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology},

pages = {1–12},

publisher = {Association for Computational Linguistics},

address = {Vancouver, Canada},

abstract = {Automatic detection of depression has attracted increasing attention from researchers in psychology, computer science, linguistics, and related disciplines. As a result, promising depression detection systems have been reported. This paper surveys these efforts by presenting the first cross-modal review of depression detection systems and discusses best practices and most promising approaches to this task.},

keywords = {Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{mell_human-like_2017,

title = {Human-Like Agents for Repeated Negotiation},

author = {Johnathan Mell},

url = {https://www.ijcai.org/proceedings/2017/754},

doi = {10.24963/ijcai.2017/754},

isbn = {978-0-9992411-0-3},

year  = {2017},

date = {2017-08-01},

booktitle = {Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence},

pages = {5189–5190},

publisher = {International Joint Conferences on Artificial Intelligence Organization},

address = {Melbourne, Australia},

abstract = {Virtual agents have been used as tools in negotiation—from acting as mediators to manifesting as full-fledged conversational partners. Virtual agents are a powerful tool for teaching negotiation skills, but require an accurate model of human behavior to perform well both as partners and teachers. The work proposed here aims to expand the current horizon of virtual negotiating agents to utilize human-like strategies. Further agents developed using this framework should be cognizant of the social factors influencing negotiation, including reputation effects and the implications of long-term repeated relationships. A roadmap of current efforts to develop agent platforms and future expansions is discussed.},

keywords = {Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{lee_dialport_2017,

title = {DialPort, Gone Live: An Update After A Year of Development},

author = {Kyusong Lee and Tiancheng Zhao and Yulun Du and Edward Cai and Allen Lu and Eli Pincus and David Traum and Stefan Ultes and Lina M. Rojas-Barahona and Milica Gasic and Steve Young and Maxine Eskenazi},

url = {http://www.sigdial.org/workshops/conference18/proceedings/index.html},

year  = {2017},

date = {2017-08-01},

booktitle = {Proceedings of the SIGDIAL 2017 Conference},

publisher = {Association for Computational Linguistics},

address = {Saarbruecken Germany},

abstract = {DialPort collects user data for connected spoken dialog systems. At present six systems are linked to a central portal that directs the user to the applicable system and suggests systems that the user may be interested in. User data has started to flow into the system.},

keywords = {Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{manuvinakurike_using_2017,

title = {Using Reinforcement Learning to Model Incrementality in a Fast-Paced Dialogue Game},

author = {Ramesh Manuvinakurike and David DeVault and Kallirroi Georgila},

url = {http://www.manuvinakurike.com/papers/eve-2017.pdf},

year  = {2017},

date = {2017-08-01},

booktitle = {Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue},

publisher = {SIGDIAL},

address = {Saarbruecken Germany},

abstract = {We apply Reinforcement Learning (RL) to the problem of incremental dialogue policy learning in the context of a fast-paced dialogue game. We compare the policy learned by RL with a high performance baseline policy which has been shown to perform very efficiently (nearly as well as humans) in this dialogue game. The RL policy outperforms the baseline policy in offline simulations (based on real user data). We provide a detailed comparison of the RL policy and the baseline policy, including information about how much effort and time it took to develop each one of them. We also highlight the cases where the RL policy performs better, and show that understanding the RL policy can provide valuable insights which can inform the creation of an even better rule-based policy.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{leuski_lessons_2017,

title = {Lessons in Dialogue System Deployment},

author = {Anton Leuski and Ron Artstein},

url = {http://www.sigdial.org/workshops/conference18/proceedings/index.html},

year  = {2017},

date = {2017-08-01},

booktitle = {Proceedings of the SIGDIAL 2017 Conference: the 18th Annual Meeting of the Special Interest Group on Discourse and Dialogue},

pages = {352–355},

publisher = {Association for Computational Linguistics},

address = {Saarbruecken Germany},

abstract = {We analyze deployment of an interactive dialogue system in an environment where deep technical expertise might not be readily available. The initial version was created using a collection of research tools. We summarize a number of challenges with its deployment at two museums and describe a new system that simplifies the installation and user interface; reduces reliance on 3rd-party software; and provides a robust data collection mechanism.},

keywords = {Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{brixey_shihbot_2017,

title = {SHIHbot: A Facebook chatbot for Sexual Health Information on HIV/AIDS},

author = {Jacqueline Brixey and Rens Hoegen and Wei Lan and Joshua Rusow and Karan Singla and Xusen Yin and Ron Artstein and Anton Leuski},

url = {http://www.sigdial.org/workshops/conference18/proceedings/index.html},

year  = {2017},

date = {2017-08-01},

booktitle = {Proceedings of the SIGDIAL 2017 Conference: the 18th Annual Meeting of the Special Interest Group on Discourse and Dialogue},

pages = {370–373},

publisher = {Association for Computational Linguistics},

address = {Saarbruecken Germany},

abstract = {We present the implementation of an autonomous chatbot, SHIHbot, deployed on Facebook, which answers a wide variety of sexual health questions on HIV/AIDS. The chatbot's response database is compiled from professional medical and public health resources in order to provide reliable information to users. The system's backend is NPCEditor, a response selection platform trained on linked questions and answers; to our knowledge this is the first retrieval-based chatbot deployed on a large public social network.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}