@inproceedings{kim-etal-2021-monah,
title = "{MONAH}: Multi-Modal Narratives for Humans to analyze conversations",
author = "Kim, Joshua Y. and
Yacef, Kalina and
Kim, Greyson and
Liu, Chunfeng and
Calvo, Rafael and
Taylor, Silas",
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.37",
doi = "10.18653/v1/2021.eacl-main.37",
pages = "466--479",
abstract = "In conversational analyses, humans manually weave multimodal information into the transcripts, which is significantly time-consuming. We introduce a system that automatically expands the verbatim transcripts of video-recorded conversations using multimodal data streams. This system uses a set of preprocessing rules to weave multimodal annotations into the verbatim transcripts and promote interpretability. Our feature engineering contributions are two-fold: firstly, we identify the range of multimodal features relevant to detect rapport-building; secondly, we expand the range of multimodal annotations and show that the expansion leads to statistically significant improvements in detecting rapport-building.",
}
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<abstract>In conversational analyses, humans manually weave multimodal information into the transcripts, which is significantly time-consuming. We introduce a system that automatically expands the verbatim transcripts of video-recorded conversations using multimodal data streams. This system uses a set of preprocessing rules to weave multimodal annotations into the verbatim transcripts and promote interpretability. Our feature engineering contributions are two-fold: firstly, we identify the range of multimodal features relevant to detect rapport-building; secondly, we expand the range of multimodal annotations and show that the expansion leads to statistically significant improvements in detecting rapport-building.</abstract>
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%0 Conference Proceedings
%T MONAH: Multi-Modal Narratives for Humans to analyze conversations
%A Kim, Joshua Y.
%A Yacef, Kalina
%A Kim, Greyson
%A Liu, Chunfeng
%A Calvo, Rafael
%A Taylor, Silas
%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 kim-etal-2021-monah
%X In conversational analyses, humans manually weave multimodal information into the transcripts, which is significantly time-consuming. We introduce a system that automatically expands the verbatim transcripts of video-recorded conversations using multimodal data streams. This system uses a set of preprocessing rules to weave multimodal annotations into the verbatim transcripts and promote interpretability. Our feature engineering contributions are two-fold: firstly, we identify the range of multimodal features relevant to detect rapport-building; secondly, we expand the range of multimodal annotations and show that the expansion leads to statistically significant improvements in detecting rapport-building.
%R 10.18653/v1/2021.eacl-main.37
%U https://aclanthology.org/2021.eacl-main.37
%U https://doi.org/10.18653/v1/2021.eacl-main.37
%P 466-479
Markdown (Informal)
[MONAH: Multi-Modal Narratives for Humans to analyze conversations](https://aclanthology.org/2021.eacl-main.37) (Kim et al., EACL 2021)
ACL
- Joshua Y. Kim, Kalina Yacef, Greyson Kim, Chunfeng Liu, Rafael Calvo, and Silas Taylor. 2021. MONAH: Multi-Modal Narratives for Humans to analyze conversations. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 466–479, Online. Association for Computational Linguistics.