Christopher Michael Mitchell

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Dialogue act modeling in task-oriented dialogue poses significant challenges. It is particularly challenging for corpora consisting of two interleaved communication streams: a dialogue stream and a task stream. In such corpora, information can be conveyed implicitly by the task stream, yielding a dialogue stream with seemingly missing information. A(More)
Learning dialogue management models poses significant challenges. In a complex task-oriented domain in which information is exchanged via parallel, interleaved dialogue and task streams, effective dialogue management models should be able to make dialogue moves based on both the dialogue and the task context. This paper presents a data-driven approach to(More)
In recent years, there have been significant advances in tutoring systems that engage students in rich natural language dialogue. With the goal of further understanding what makes tutorial dialogue successful, this article presents a corpus-based approach to modelling the differential effectiveness of tutorial dialogue strategies with respect to learning.(More)
Designing dialogue systems that engage in rich tutorial dialogue has long been a goal of the intelligent tutoring systems community. A key challenge for these systems is determining when to intervene during student problem solving. Although intervention strategies have historically been hand-authored, utilizing machine learning to automatically acquire(More)
One-on-one tutoring is significantly more effective than traditional classroom instruction. In recent years, automated tutoring systems are approaching that level of effectiveness by engaging students in rich natural language dialogue that contributes to learning. A promising approach for further improving the effectiveness of tutorial dialogue systems is(More)
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(More)
Current automated assessment techniques for English language learners evaluate comprehension and synthesis skills via written text or one-turn spoken responses, measuring essential skills needed for academic and professional environments. However, as these current tests do not include dialogue-based components , they cannot provide insight into the(More)
Designing dialogue systems that engage in rich tutorial dialogue has long been a goal of the intelligent tutoring systems community. A key challenge for these systems is determining when to intervene during student problem solving. Although intervention strategies have historically been hand-authored, utilizing machine learning to automatically acquire(More)
The task of narrative visualization has been the subject of increasing interest in recent years. Much like data visualization, narrative visualization offers users an informative and aesthetically pleasing perspective on " story data. " Automatically creating visual representations of narratives poses significant computational challenges due to the complex(More)
Introductory computer science courses cultivate the next generation of computer scientists. The impressions students take away from these courses are crucial, setting the tone for the rest of the students' computer science education. It is known that students struggle with many concepts central to computer science, struggles that could be alleviated in part(More)