|Aysu Ezen-Can and Kristy Boyer
Unsupervised machine learning approaches hold great promise for recognizing dialogue acts, but the performance of these models tends to be much lower than the accuracies reached by supervised models. However, some dialogues, such as task-oriented dialogues with parallel task streams, hold rich information that has not yet been leveraged within unsupervised dialogue act models. This paper investigates incorporating task features into an unsupervised dialogue act model trained on a corpus of human tutoring in introductory computer science. Experimental results show that incorporating task features and dialogue history features significantly improve unsupervised dialogue act classification, particularly within a hierarchical framework that gives prominence to dialogue history. This work constitutes a step toward building high-performing unsupervised dialogue act models that will be used in the next generation of task-oriented dialogue systems.