Preliminary results on dialogue act classification in chat-based online tutorial dialogues


We present in this paper preliminary results with dialogue act classification in human-to-human tutorial dialogues. Dialogue acts are ways to characterize the intentions and actions of the speakers in dialogues based on the language-as-action theory. This work serves our larger goal of identifying patterns of tutors’ actions, in the form of dialogue act and subact sequences, that relate to various aspects of learning. The preliminary results we obtained for dialogue act classification using a supervised machine learning approach are promising.

Publication Title

Proceedings of the 9th International Conference on Educational Data Mining, EDM 2016

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