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 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.- Anthology ID:
- 2021.eacl-main.94
- Volume:
- Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Editors:
- Paola Merlo, Jorg Tiedemann, Reut Tsarfaty
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1103–1120
- Language:
- URL:
- https://aclanthology.org/2021.eacl-main.94
- DOI:
- 10.18653/v1/2021.eacl-main.94
- Cite (ACL):
- Dian Yu and Zhou Yu. 2021. MIDAS: A Dialog Act Annotation Scheme for Open Domain HumanMachine Spoken Conversations. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1103–1120, Online. Association for Computational Linguistics.
- Cite (Informal):
- MIDAS: A Dialog Act Annotation Scheme for Open Domain HumanMachine Spoken Conversations (Yu & Yu, EACL 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2021.eacl-main.94.pdf