  NLP systems rarely give special consideration to numbers found in text. This
starkly contrasts with the consensus in neuroscience that, in the brain,
numbers are represented differently from words. We arrange recent NLP work on
numeracy into a comprehensive taxonomy of tasks and methods. We break down the
subjective notion of numeracy into 7 subtasks, arranged along two dimensions:
granularity (exact vs approximate) and units (abstract vs grounded). We analyze
the myriad representational choices made by 18 previously published number
encoders and decoders. We synthesize best practices for representing numbers in
text and articulate a vision for holistic numeracy in NLP, comprised of design
trade-offs and a unified evaluation.
