<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="sachan-xing-2017-learning">
    <titleInfo>
        <title>Learning to Solve Geometry Problems from Natural Language Demonstrations in Textbooks</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Mrinmaya</namePart>
        <namePart type="family">Sachan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Eric</namePart>
        <namePart type="family">Xing</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2017-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Nancy</namePart>
            <namePart type="family">Ide</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Aurélie</namePart>
            <namePart type="family">Herbelot</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Lluís</namePart>
            <namePart type="family">Màrquez</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Vancouver, Canada</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Humans as well as animals are good at imitation. Inspired by this, the learning by demonstration view of machine learning learns to perform a task from detailed example demonstrations. In this paper, we introduce the task of question answering using natural language demonstrations where the question answering system is provided with detailed demonstrative solutions to questions in natural language. As a case study, we explore the task of learning to solve geometry problems using demonstrative solutions available in textbooks. We collect a new dataset of demonstrative geometry solutions from textbooks and explore approaches that learn to interpret these demonstrations as well as to use these interpretations to solve geometry problems. Our approaches show improvements over the best previously published system for solving geometry problems.</abstract>
    <identifier type="citekey">sachan-xing-2017-learning</identifier>
    <identifier type="doi">10.18653/v1/S17-1029</identifier>
    <location>
        <url>https://aclanthology.org/S17-1029</url>
    </location>
    <part>
        <date>2017-08</date>
        <extent unit="page">
            <start>251</start>
            <end>261</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Learning to Solve Geometry Problems from Natural Language Demonstrations in Textbooks
%A Sachan, Mrinmaya
%A Xing, Eric
%Y Ide, Nancy
%Y Herbelot, Aurélie
%Y Màrquez, Lluís
%S Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F sachan-xing-2017-learning
%X Humans as well as animals are good at imitation. Inspired by this, the learning by demonstration view of machine learning learns to perform a task from detailed example demonstrations. In this paper, we introduce the task of question answering using natural language demonstrations where the question answering system is provided with detailed demonstrative solutions to questions in natural language. As a case study, we explore the task of learning to solve geometry problems using demonstrative solutions available in textbooks. We collect a new dataset of demonstrative geometry solutions from textbooks and explore approaches that learn to interpret these demonstrations as well as to use these interpretations to solve geometry problems. Our approaches show improvements over the best previously published system for solving geometry problems.
%R 10.18653/v1/S17-1029
%U https://aclanthology.org/S17-1029
%U https://doi.org/10.18653/v1/S17-1029
%P 251-261

Markdown (Informal)

[Learning to Solve Geometry Problems from Natural Language Demonstrations in Textbooks](https://aclanthology.org/S17-1029) (Sachan & Xing, *SEM 2017)

• Learning to Solve Geometry Problems from Natural Language Demonstrations in Textbooks (Sachan & Xing, *SEM 2017)

ACL

• Mrinmaya Sachan and Eric Xing. 2017. Learning to Solve Geometry Problems from Natural Language Demonstrations in Textbooks. In Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017), pages 251–261, Vancouver, Canada. Association for Computational Linguistics.