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.
@article{arxiv.2103.13136,
title = {Representing Numbers in NLP: a Survey and a Vision},
author = {Avijit Thawani and Jay Pujara and Pedro A. Szekely and Filip Ilievski},
journal= {arXiv preprint arXiv:2103.13136},
year = {2021}
}