  While natural language understanding (NLU) is advancing rapidly, today's
technology differs from human-like language understanding in fundamental ways,
notably in its inferior efficiency, interpretability, and generalization. This
work proposes an approach to representation and learning based on the tenets of
embodied cognitive linguistics (ECL). According to ECL, natural language is
inherently executable (like programming languages), driven by mental simulation
and metaphoric mappings over hierarchical compositions of structures and
schemata learned through embodied interaction. This position paper argues that
the use of grounding by metaphoric inference and simulation will greatly
benefit NLU systems, and proposes a system architecture along with a roadmap
towards realizing this vision.
