Work

Toward incremental dialogue act segmentation in fast-paced interactive dialogue systems

Year: 2016

Type: conference-paper

Abstract: 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.

Source: Annual Meeting of the Special Interest Group on Discourse and Dialogue

DOI: 10.18653/v1/w16-3632

Authors: Ramesh Manuvinakurike, Maike Paetzel, Cheng Qu, David Schlangen, David DeVault

Institutions: USC Institute for Creative Technologies, Uppsala University, Bielefeld University

Language: English

Topic: Speech and dialogue systems

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Science Foundation, Deutsche Forschungsgemeinschaft, Universität Bielefeld

Awards: G1927681210, G3429453619, RI: Small: Incremental Speech Processing for Rapid Dialogue