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Representing Numbers in NLP: a Survey and a Vision

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arxiv 2103.13136 v1 pith:OYGGAZ2Z submitted 2021-03-24 cs.CL cs.AIcs.LG

Avijit Thawani , Jay Pujara , Pedro A. Szekely , Filip Ilievski This is my paper · ORCID
classification cs.CLcs.AIcs.LG
keywords numbersnumeracyrepresentingtextvisionabstractalonganalyze
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. IGC: Integrating a Gated Calculator into an LLM to Solve Arithmetic Tasks Reliably and Efficiently

    cs.LG 2025-01 conditional novelty 8.0 of 10

    A gated calculator module inserted into a frozen Llama 3.1 8B model enables near-perfect arithmetic on BigBench, including multiplication, in a single forward pass without external tools.

  2. UncertaintyVis: Preserving Linguistic Uncertainty in Automated Text-to-Chart Generation

    cs.HC 2026-08 conditional novelty 6.0 of 10

    A text-to-chart system that visually preserves linguistic hedges helps trained readers match charts to uncertain text, though the cognitive-load evidence is not statistically significant.

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