Abstract
In this paper we argue that embodied multimodal agents, i.e., avatars, can play an important role in moving natural language processing toward “deep understanding.” Fully-featured interactive agents, model encounters between two “people,” but a language-only agent has little environmental and situational awareness. Multimodal agents bring new opportunities for interpreting visuals, locational information, gestures, etc., which are more axes along which to communicate. We propose that multimodal agents, by facilitating an embodied form of human-computer interaction, provide additional structure that can be used to train models that move NLP systems closer to genuine “understanding” of grounded language, and we discuss ongoing studies using existing systems.- Anthology ID:
- 2021.hcinlp-1.7
- Volume:
- Proceedings of the First Workshop on Bridging Human–Computer Interaction and Natural Language Processing
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Editors:
- Su Lin Blodgett, Michael Madaio, Brendan O'Connor, Hanna Wallach, Qian Yang
- Venue:
- HCINLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 41–46
- Language:
- URL:
- https://aclanthology.org/2021.hcinlp-1.7
- DOI:
- Cite (ACL):
- Nikhil Krishnaswamy and Nada Alalyani. 2021. Embodied Multimodal Agents to Bridge the Understanding Gap. In Proceedings of the First Workshop on Bridging Human–Computer Interaction and Natural Language Processing, pages 41–46, Online. Association for Computational Linguistics.
- Cite (Informal):
- Embodied Multimodal Agents to Bridge the Understanding Gap (Krishnaswamy & Alalyani, HCINLP 2021)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/2021.hcinlp-1.7.pdf