Abstract
Videos of group interactions contain a wealth of information beyond the information directly communicated in a transcript of the discussion. Tracking who has participated throughout an extended interaction and what each of their trajectories has been in relation to one another is the foundation for joint activity understanding, though it comes with some unique challenges in videos of tightly coupled group work. Motivated by insights into the properties of such scenarios, including group composition and the properties of task-oriented, goal directed tasks, we present a successful proof-of-concept. In particular, we present a transfer experiment to a dyadic robot construction task, an ablation study, and a qualitative analysis.- Anthology ID:
- 2022.tu-1.3
- Volume:
- Proceedings of the First Workshop On Transcript Understanding
- Month:
- Oct
- Year:
- 2022
- Address:
- Gyeongju, South Korea
- Editors:
- Franck Dernoncourt, Thien Huu Nguyen, Viet Dac Lai, Amir Pouran Ben Veyseh, Trung H. Bui, David Seunghyun Yoon
- Venue:
- TU
- SIG:
- Publisher:
- International Conference on Computational Linguistics
- Note:
- Pages:
- 20–29
- Language:
- URL:
- https://aclanthology.org/2022.tu-1.3
- DOI:
- Cite (ACL):
- Sumit Agarwal, Rosanna Vitiello, and Carolyn Rosé. 2022. Model Transfer for Event tracking as Transcript Understanding for Videos of Small Group Interaction. In Proceedings of the First Workshop On Transcript Understanding, pages 20–29, Gyeongju, South Korea. International Conference on Computational Linguistics.
- Cite (Informal):
- Model Transfer for Event tracking as Transcript Understanding for Videos of Small Group Interaction (Agarwal et al., TU 2022)
- PDF:
- https://preview.aclanthology.org/corrections-2024-04/2022.tu-1.3.pdf