High frequency word entrainment in spoken dialogue
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Dialog Convergence and Learning
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Lexical and syntactic priming and their impact in deployed spoken dialog systems
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
The development and evaluation of a survey to measure user engagement
Journal of the American Society for Information Science and Technology
Talk like an electrician: student dialogue mimicking behavior in an intelligent tutoring system
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
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Convergence is thought to be an important phenomenon in dialogue through which interlocutors adapt to each other. Yet, its mechanisms and relationship to dialogue outcomes are not fully understood. This paper explores convergence in textual task-oriented dialogue during a longitudinal study. The results suggest that over time, convergence between interlocutors increases with successive dialogues. Additionally, for the tutorial dialogue domain at hand, convergence metrics were found to be significant predictors of dialogue outcomes such as learning, mental effort, and emotional states including frustration, boredom, and confusion. The results suggest ways in which dialogue systems may leverage convergence to enhance their interactions with users.