MIDAS: A Dialog Act Annotation Scheme for Open Domain HumanMachine Spoken Conversations
Dian Yu, Zhou Yu
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 1 Code, data, and trained models are available at https: