期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2021
卷号：2021
页码：1103-1120
DOI：10.18653/v1/2021.eacl-main.94
语种：English
出版社：ACL Anthology
摘要：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 human-human 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.
