  Language understanding research is held back by a failure to relate language
to the physical world it describes and to the social interactions it
facilitates. Despite the incredible effectiveness of language processing models
to tackle tasks after being trained on text alone, successful linguistic
communication relies on a shared experience of the world. It is this shared
experience that makes utterances meaningful.
  Natural language processing is a diverse field, and progress throughout its
development has come from new representational theories, modeling techniques,
data collection paradigms, and tasks. We posit that the present success of
representation learning approaches trained on large, text-only corpora requires
the parallel tradition of research on the broader physical and social context
of language to address the deeper questions of communication.
