Year: 2021
Type: conference-paper
Abstract: Dialog act prediction in open-domain conversations is an essential language comprehension task for both dialog system building and discourse analysis.Previous dialog act schemes, such as SWBD-DAMSL, are designed mainly for discourse analysis in humanhuman conversations.In this paper, we present a dialog act annotation scheme, MIDAS (Machine Interaction Dialog Act Scheme), targeted at open-domain human-machine conversations.MIDAS is designed to assist machines to improve their ability to understand human partners.MIDAS has a hierarchical structure and supports multi-label annotations.We collected and annotated a large open-domain human-machine spoken conversation dataset (consisting of 24K utterances).To validate our scheme, we leveraged transfer learning methods to train a multi-label dialog act prediction model and reached an F1 score of 0.79. 1
Institutions: University of California, Davis, Columbia University
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Funder: National Science Foundation
OpenAlex ID: W3152908575