ACM Student Research Competition
Specification and implementation of flexible human-computer dialogs is challenging because of the complexity involved in rendering the dialog responsive to a vast number of varied paths through which users might desire to complete the dialog. To address this problem, we developed a toolkit for modeling and implementing task-based, mixed-initiative dialogs based on metaphors from lambda calculus. Our toolkit can automatically operationalize a dialog that involves multiple prompts and/or sub-dialogs, given a high-level dialog specification of it. The use of natural language with the resulting dialogs makes the flexibility in communicating user utterances commensurate with that in dialog completion paths—an aspect missing from commercial assistants like Siri. Our results demonstrate that the dialogs authored with our toolkit support the end user’s completion of a human-computer dialog in a manner that is most natural to them—in a mixed-initiative fashion—that resembles human-human interaction.
Bag of words model, function currying, functional programming, human-computer dialogs, interactive voice response systems, k-nearest-neighbor classifier, lambda calculus, mixed-initiative dialogs, mixed-initiative interaction, natural language processing, partial evaluation.
Buck, J.W., Perugini, S., & Nguyen, T.V. (2017). CSE: U: Mixed-initiative personal assistants. ACM Student Research Competition Grand Finals Candidates, 2016-2017; Undergraduate Student Winners.