|Ramesh Manuvinakurike, Maike Paetzel, Cheng Qu, David Schlangen and David DeVault|
In this paper, we present and evaluate an approach to incremental dialogue act (DA) segmentation and classification. Our approach utilizes prosodic, lexico-syntactic and contextual features, and achieves an encouraging level of performance in offline corpus-based evaluation as well as in simulated human-agent dialogues. Our approach uses a pipeline of sequential processing steps, and we investigate the contribution of different processing steps to DA segmentation errors. We present our results using both existing and new metrics for DA segmentation. The incremental DA segmentation capability described here may help future systems to allow more natural speech from users and enable more natural patterns of interaction.