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    <titleInfo>
        <title>Parsing Algebraic Word Problems into Equations</title>
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        <namePart type="given">Hannaneh</namePart>
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    <name type="personal">
        <namePart type="given">Ashish</namePart>
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        <namePart type="given">Oren</namePart>
        <namePart type="family">Etzioni</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00160</identifier>
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        <date>2015</date>
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            <start>585</start>
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%0 Journal Article
%T Parsing Algebraic Word Problems into Equations
%A Koncel-Kedziorski, Rik
%A Hajishirzi, Hannaneh
%A Sabharwal, Ashish
%A Etzioni, Oren
%A Ang, Siena Dumas
%J Transactions of the Association for Computational Linguistics
%D 2015
%V 3
%I MIT Press
%C Cambridge, MA
%F koncel-kedziorski-etal-2015-parsing
%X 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.
%R 10.1162/tacl_a_00160
%U https://aclanthology.org/Q15-1042
%U https://doi.org/10.1162/tacl_a_00160
%P 585-597

Markdown (Informal)

[Parsing Algebraic Word Problems into Equations](https://aclanthology.org/Q15-1042) (Koncel-Kedziorski et al., TACL 2015)

• Parsing Algebraic Word Problems into Equations (Koncel-Kedziorski et al., TACL 2015)

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.