Parsing Algebraic Word Problems into Equations

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.