<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="kashefi-etal-2023-argument">
    <titleInfo>
        <title>Argument Detection in Student Essays under Resource Constraints</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Omid</namePart>
        <namePart type="family">Kashefi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sophia</namePart>
        <namePart type="family">Chan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Swapna</namePart>
        <namePart type="family">Somasundaran</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 10th Workshop on Argument Mining</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Milad</namePart>
            <namePart type="family">Alshomary</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Chung-Chi</namePart>
            <namePart type="family">Chen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Smaranda</namePart>
            <namePart type="family">Muresan</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Joonsuk</namePart>
            <namePart type="family">Park</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Julia</namePart>
            <namePart type="family">Romberg</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Singapore</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Learning to make effective arguments is vital for the development of critical-thinking in students and, hence, for their academic and career success. Detecting argument components is crucial for developing systems that assess students’ ability to develop arguments. Traditionally, supervised learning has been used for this task, but this requires a large corpus of reliable training examples which are often impractical to obtain for student writing. Large language models have also been shown to be effective few-shot learners, making them suitable for low-resource argument detection. However, concerns such as latency, service reliability, and data privacy might hinder their practical applicability. To address these challenges, we present a low-resource classification approach that combines the intrinsic entailment relationship among the argument elements with a parameter-efficient prompt-tuning strategy. Experimental results demonstrate the effectiveness of our method in reducing the data and computation requirements of training an argument detection model without compromising the prediction accuracy. This suggests the practical applicability of our model across a variety of real-world settings, facilitating broader access to argument classification for researchers spanning various domains and problem scenarios.</abstract>
    <identifier type="citekey">kashefi-etal-2023-argument</identifier>
    <identifier type="doi">10.18653/v1/2023.argmining-1.7</identifier>
    <location>
        <url>https://aclanthology.org/2023.argmining-1.7/</url>
    </location>
    <part>
        <date>2023-12</date>
        <extent unit="page">
            <start>64</start>
            <end>75</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Argument Detection in Student Essays under Resource Constraints
%A Kashefi, Omid
%A Chan, Sophia
%A Somasundaran, Swapna
%Y Alshomary, Milad
%Y Chen, Chung-Chi
%Y Muresan, Smaranda
%Y Park, Joonsuk
%Y Romberg, Julia
%S Proceedings of the 10th Workshop on Argument Mining
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F kashefi-etal-2023-argument
%X Learning to make effective arguments is vital for the development of critical-thinking in students and, hence, for their academic and career success. Detecting argument components is crucial for developing systems that assess students’ ability to develop arguments. Traditionally, supervised learning has been used for this task, but this requires a large corpus of reliable training examples which are often impractical to obtain for student writing. Large language models have also been shown to be effective few-shot learners, making them suitable for low-resource argument detection. However, concerns such as latency, service reliability, and data privacy might hinder their practical applicability. To address these challenges, we present a low-resource classification approach that combines the intrinsic entailment relationship among the argument elements with a parameter-efficient prompt-tuning strategy. Experimental results demonstrate the effectiveness of our method in reducing the data and computation requirements of training an argument detection model without compromising the prediction accuracy. This suggests the practical applicability of our model across a variety of real-world settings, facilitating broader access to argument classification for researchers spanning various domains and problem scenarios.
%R 10.18653/v1/2023.argmining-1.7
%U https://aclanthology.org/2023.argmining-1.7/
%U https://doi.org/10.18653/v1/2023.argmining-1.7
%P 64-75

Markdown (Informal)

[Argument Detection in Student Essays under Resource Constraints](https://aclanthology.org/2023.argmining-1.7/) (Kashefi et al., ArgMining 2023)

• Argument Detection in Student Essays under Resource Constraints (Kashefi et al., ArgMining 2023)

ACL

• Omid Kashefi, Sophia Chan, and Swapna Somasundaran. 2023. Argument Detection in Student Essays under Resource Constraints. In Proceedings of the 10th Workshop on Argument Mining, pages 64–75, Singapore. Association for Computational Linguistics.