Representing Numbers in NLP: a Survey and a Vision

Avijit Thawani, Jay Pujara, Filip Ilievski, Pedro Szekely


Abstract
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 over a dozen 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.
Anthology ID:
2021.naacl-main.53
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
644–656
Language:
URL:
https://aclanthology.org/2021.naacl-main.53
DOI:
10.18653/v1/2021.naacl-main.53
Bibkey:
Cite (ACL):
Avijit Thawani, Jay Pujara, Filip Ilievski, and Pedro Szekely. 2021. Representing Numbers in NLP: a Survey and a Vision. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 644–656, Online. Association for Computational Linguistics.
Cite (Informal):
Representing Numbers in NLP: a Survey and a Vision (Thawani et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.53.pdf
Video:
 https://aclanthology.org/2021.naacl-main.53.mp4
Data
DROPGLUEMATHNumerSense