  The last years have shown rapid developments in the field of multimodal
machine learning, combining e.g., vision, text or speech. In this position
paper we explain how the field uses outdated definitions of multimodality that
prove unfit for the machine learning era. We propose a new task-relative
definition of (multi)modality in the context of multimodal machine learning
that focuses on representations and information that are relevant for a given
machine learning task. With our new definition of multimodality we aim to
provide a missing foundation for multimodal research, an important component of
language grounding and a crucial milestone towards NLU.
