Parsing Algebraic Word Problems into Equations

Rik Koncel-Kedziorski, Hannaneh Hajishirzi, Ashish Sabharwal, Oren Etzioni, Siena Dumas Ang


Abstract
This paper formalizes the problem of solving multi-sentence algebraic word problems as that of generating and scoring equation trees. We use integer linear programming to generate equation trees and score their likelihood by learning local and global discriminative models. These models are trained on a small set of word problems and their answers, without any manual annotation, in order to choose the equation that best matches the problem text. We refer to the overall system as Alges. We compare Alges with previous work and show that it covers the full gamut of arithmetic operations whereas Hosseini et al. (2014) only handle addition and subtraction. In addition, Alges overcomes the brittleness of the Kushman et al. (2014) approach on single-equation problems, yielding a 15% to 50% reduction in error.
Anthology ID:
Q15-1042
Volume:
Transactions of the Association for Computational Linguistics, Volume 3
Month:
Year:
2015
Address:
Cambridge, MA
Editors:
Michael Collins, Lillian Lee
Venue:
TACL
SIG:
Publisher:
MIT Press
Note:
Pages:
585–597
Language:
URL:
https://aclanthology.org/Q15-1042
DOI:
10.1162/tacl_a_00160
Bibkey:
Cite (ACL):
Rik Koncel-Kedziorski, Hannaneh Hajishirzi, Ashish Sabharwal, Oren Etzioni, and Siena Dumas Ang. 2015. Parsing Algebraic Word Problems into Equations. Transactions of the Association for Computational Linguistics, 3:585–597.
Cite (Informal):
Parsing Algebraic Word Problems into Equations (Koncel-Kedziorski et al., TACL 2015)
Copy Citation:
PDF:
https://aclanthology.org/Q15-1042.pdf