Representing Numbers in NLP: a Survey and a Vision

NAACL 2021  ·  Avijit Thawani, Jay Pujara, Pedro A. Szekely, Filip Ilievski ·

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.

PDF Abstract NAACL 2021 PDF NAACL 2021 Abstract

Code
Add Remove Mark official

No code implementations yet. Submit your code now

Tasks
Add Remove

Results from the Paper
Edit

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods
Add Remove

No methods listed for this paper. Add relevant methods here
Contact us on: hello@paperswithcode.com . Papers With Code is a free resource with all data licensed under CC-BY-SA.
Terms Data policy Cookies policy