Are NLP Models really able to Solve Simple Math Word Problems?

Arkil Patel, Satwik Bhattamishra, Navin Goyal


Abstract
The problem of designing NLP solvers for math word problems (MWP) has seen sustained research activity and steady gains in the test accuracy. Since existing solvers achieve high performance on the benchmark datasets for elementary level MWPs containing one-unknown arithmetic word problems, such problems are often considered “solved” with the bulk of research attention moving to more complex MWPs. In this paper, we restrict our attention to English MWPs taught in grades four and lower. We provide strong evidence that the existing MWP solvers rely on shallow heuristics to achieve high performance on the benchmark datasets. To this end, we show that MWP solvers that do not have access to the question asked in the MWP can still solve a large fraction of MWPs. Similarly, models that treat MWPs as bag-of-words can also achieve surprisingly high accuracy. Further, we introduce a challenge dataset, SVAMP, created by applying carefully chosen variations over examples sampled from existing datasets. The best accuracy achieved by state-of-the-art models is substantially lower on SVAMP, thus showing that much remains to be done even for the simplest of the MWPs.
Anthology ID:
2021.naacl-main.168
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:
2080–2094
Language:
URL:
https://aclanthology.org/2021.naacl-main.168
DOI:
10.18653/v1/2021.naacl-main.168
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.168.pdf
Optional supplementary code:
 2021.naacl-main.168.OptionalSupplementaryCode.zip
Optional supplementary data:
 2021.naacl-main.168.OptionalSupplementaryData.zip
Code
 arkilpatel/SVAMP
Data
SVAMP