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 langu
age-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.
