<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="griffith-kalita-2020-solving">
    <titleInfo>
        <title>Solving Arithmetic Word Problems Using Transformer and Pre-processing of Problem Texts</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Kaden</namePart>
        <namePart type="family">Griffith</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jugal</namePart>
        <namePart type="family">Kalita</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 17th International Conference on Natural Language Processing (ICON)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Pushpak</namePart>
            <namePart type="family">Bhattacharyya</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Dipti</namePart>
            <namePart type="given">Misra</namePart>
            <namePart type="family">Sharma</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Rajeev</namePart>
            <namePart type="family">Sangal</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>NLP Association of India (NLPAI)</publisher>
            <place>
                <placeTerm type="text">Indian Institute of Technology Patna, Patna, India</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>This paper outlines the use of Transformer networks trained to translate math word problems to equivalent arithmetic expressions in infix, prefix, and postfix notations. We compare results produced by a large number of neural configurations and find that most configurations outperform previously reported approaches on three of four datasets with significant increases in accuracy of over 20 percentage points. The best neural approaches boost accuracy by 30% on average when compared to the previous state-of-the-art.</abstract>
    <identifier type="citekey">griffith-kalita-2020-solving</identifier>
    <location>
        <url>https://aclanthology.org/2020.icon-main.10</url>
    </location>
    <part>
        <date>2020-12</date>
        <extent unit="page">
            <start>76</start>
            <end>84</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Solving Arithmetic Word Problems Using Transformer and Pre-processing of Problem Texts
%A Griffith, Kaden
%A Kalita, Jugal
%Y Bhattacharyya, Pushpak
%Y Sharma, Dipti Misra
%Y Sangal, Rajeev
%S Proceedings of the 17th International Conference on Natural Language Processing (ICON)
%D 2020
%8 December
%I NLP Association of India (NLPAI)
%C Indian Institute of Technology Patna, Patna, India
%F griffith-kalita-2020-solving
%X This paper outlines the use of Transformer networks trained to translate math word problems to equivalent arithmetic expressions in infix, prefix, and postfix notations. We compare results produced by a large number of neural configurations and find that most configurations outperform previously reported approaches on three of four datasets with significant increases in accuracy of over 20 percentage points. The best neural approaches boost accuracy by 30% on average when compared to the previous state-of-the-art.
%U https://aclanthology.org/2020.icon-main.10
%P 76-84

Markdown (Informal)

[Solving Arithmetic Word Problems Using Transformer and Pre-processing of Problem Texts](https://aclanthology.org/2020.icon-main.10) (Griffith & Kalita, ICON 2020)

• Solving Arithmetic Word Problems Using Transformer and Pre-processing of Problem Texts (Griffith & Kalita, ICON 2020)

ACL

• Kaden Griffith and Jugal Kalita. 2020. Solving Arithmetic Word Problems Using Transformer and Pre-processing of Problem Texts. In Proceedings of the 17th International Conference on Natural Language Processing (ICON), pages 76–84, Indian Institute of Technology Patna, Patna, India. NLP Association of India (NLPAI).