When Text and Speech are Not Enough: A Multimodal Dataset of Collaboration in a Situated Task

To adequately model information exchanged in real human-human interactions, considering speech or text alone leaves out many critical modalities. The channels contributing to the “making of sense” in human-human interactions include but are not limited to gesture, speech, user-interaction modeling, gaze, joint attention, and involvement/engagement, all of which need to be adequately modeled to automatically extract correct and meaningful information. In this paper, we present a multimodal dataset of a novel situated and shared collaborative task, with the above channels annotated to encode these different aspects of the situated and embodied involvement of the participants in the joint activity.