@inproceedings{yu-yu-2021-midas,
title = "{MIDAS}: A Dialog Act Annotation Scheme for Open Domain {H}uman{M}achine Spoken Conversations",
author = "Yu, Dian and
Yu, Zhou",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.94",
doi = "10.18653/v1/2021.eacl-main.94",
pages = "1103--1120",
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.",
}
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%0 Conference Proceedings
%T MIDAS: A Dialog Act Annotation Scheme for Open Domain HumanMachine Spoken Conversations
%A Yu, Dian
%A Yu, Zhou
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F yu-yu-2021-midas
%X 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.
%R 10.18653/v1/2021.eacl-main.94
%U https://aclanthology.org/2021.eacl-main.94
%U https://doi.org/10.18653/v1/2021.eacl-main.94
%P 1103-1120
Markdown (Informal)
[MIDAS: A Dialog Act Annotation Scheme for Open Domain HumanMachine Spoken Conversations](https://aclanthology.org/2021.eacl-main.94) (Yu & Yu, EACL 2021)
ACL