<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="lugini-litman-2020-contextual">
    <titleInfo>
        <title>Contextual Argument Component Classification for Class Discussions</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Luca</namePart>
        <namePart type="family">Lugini</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Diane</namePart>
        <namePart type="family">Litman</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 28th International Conference on Computational Linguistics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Donia</namePart>
            <namePart type="family">Scott</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Nuria</namePart>
            <namePart type="family">Bel</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Chengqing</namePart>
            <namePart type="family">Zong</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>International Committee on Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Barcelona, Spain (Online)</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Argument mining systems often consider contextual information, i.e. information outside of an argumentative discourse unit, when trained to accomplish tasks such as argument component identification, classification, and relation extraction. However, prior work has not carefully analyzed the utility of different contextual properties in context-aware models. In this work, we show how two different types of contextual information, local discourse context and speaker context, can be incorporated into a computational model for classifying argument components in multi-party classroom discussions. We find that both context types can improve performance, although the improvements are dependent on context size and position.</abstract>
    <identifier type="citekey">lugini-litman-2020-contextual</identifier>
    <identifier type="doi">10.18653/v1/2020.coling-main.128</identifier>
    <location>
        <url>https://aclanthology.org/2020.coling-main.128</url>
    </location>
    <part>
        <date>2020-12</date>
        <extent unit="page">
            <start>1475</start>
            <end>1480</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Contextual Argument Component Classification for Class Discussions
%A Lugini, Luca
%A Litman, Diane
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F lugini-litman-2020-contextual
%X Argument mining systems often consider contextual information, i.e. information outside of an argumentative discourse unit, when trained to accomplish tasks such as argument component identification, classification, and relation extraction. However, prior work has not carefully analyzed the utility of different contextual properties in context-aware models. In this work, we show how two different types of contextual information, local discourse context and speaker context, can be incorporated into a computational model for classifying argument components in multi-party classroom discussions. We find that both context types can improve performance, although the improvements are dependent on context size and position.
%R 10.18653/v1/2020.coling-main.128
%U https://aclanthology.org/2020.coling-main.128
%U https://doi.org/10.18653/v1/2020.coling-main.128
%P 1475-1480

Markdown (Informal)

[Contextual Argument Component Classification for Class Discussions](https://aclanthology.org/2020.coling-main.128) (Lugini & Litman, COLING 2020)

• Contextual Argument Component Classification for Class Discussions (Lugini & Litman, COLING 2020)

ACL

• Luca Lugini and Diane Litman. 2020. Contextual Argument Component Classification for Class Discussions. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1475–1480, Barcelona, Spain (Online). International Committee on Computational Linguistics.