Multimodal Modeling of Task-Mediated Confusion

Camille Mince, Skye Rhomberg, Cecilia Alm, Reynold Bailey, Alex Ororbia


Abstract
In order to build more human-like cognitive agents, systems capable of detecting various human emotions must be designed to respond appropriately. Confusion, the combination of an emotional and cognitive state, is under-explored. In this paper, we build upon prior work to develop models that detect confusion from three modalities: video (facial features), audio (prosodic features), and text (transcribed speech features). Our research improves the data collection process by allowing for continuous (as opposed to discrete) annotation of confusion levels. We also craft models based on recurrent neural networks (RNNs) given their ability to predict sequential data. In our experiments, we find that text and video modalities are the most important in predicting confusion while the explored audio features are relatively unimportant predictors of confusion in our data.
Anthology ID:
2022.naacl-srw.24
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop
Month:
July
Year:
2022
Address:
Hybrid: Seattle, Washington + Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
188–194
Language:
URL:
https://aclanthology.org/2022.naacl-srw.24
DOI:
10.18653/v1/2022.naacl-srw.24
Bibkey:
Cite (ACL):
Camille Mince, Skye Rhomberg, Cecilia Alm, Reynold Bailey, and Alex Ororbia. 2022. Multimodal Modeling of Task-Mediated Confusion. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop, pages 188–194, Hybrid: Seattle, Washington + Online. Association for Computational Linguistics.
Cite (Informal):
Multimodal Modeling of Task-Mediated Confusion (Mince et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-srw.24.pdf
Video:
 https://aclanthology.org/2022.naacl-srw.24.mp4