PARADISE: a framework for evaluating spoken dialogue agents
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Comparing objective and subjective measures of usability in a human-robot dialogue system
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
A probabilistic multimodal approach for predicting listener backchannels
Autonomous Agents and Multi-Agent Systems
Turn-taking cues in task-oriented dialogue
Computer Speech and Language
Talking with robots about objects: a system-level evaluation in HRI
HRI '12 Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction
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During conversations, addressees produce conversational acts---verbal and nonverbal backchannels---that facilitate turn-taking, acknowledge speakership, and communicate common ground without disrupting the speaker's speech. These acts play a key role in achieving fluent conversations. Therefore, gaining a deeper understanding of how these acts interact with speaker behaviors in shaping conversations might offer key insights into the design of technologies such as computer-mediated communication systems and embodied conversational agents. In this paper, we explore how a regression-based approach might offer such insights into modeling predictive relationships between speaker behaviors and addressee backchannels in a storytelling scenario. Our results reveal speaker eye contact as a significant predictor of verbal, nonverbal, and bimodal backchannels and utterance boundaries as predictors of nonverbal and bimodal backchannels.