MONAH: Multi-Modal Narratives for Humans to analyze conversations
Joshua Y. Kim, Kalina Yacef, Greyson Kim, Chunfeng Liu, Rafael Calvo, Silas Taylor
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.- Anthology ID:
- 2021.eacl-main.37
- Volume:
- Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 466–479
- Language:
- URL:
- https://aclanthology.org/2021.eacl-main.37
- DOI:
- 10.18653/v1/2021.eacl-main.37
- Cite (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.
- Cite (Informal):
- MONAH: Multi-Modal Narratives for Humans to analyze conversations (Kim et al., EACL 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.eacl-main.37.pdf
- Code
- SpectData/MONAH