<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="chen-etal-2023-fastkassim">
    <titleInfo>
        <title>FastKASSIM: A Fast Tree Kernel-Based Syntactic Similarity Metric</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Maximillian</namePart>
        <namePart type="family">Chen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Caitlyn</namePart>
        <namePart type="family">Chen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Xiao</namePart>
        <namePart type="family">Yu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Zhou</namePart>
        <namePart type="family">Yu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Andreas</namePart>
            <namePart type="family">Vlachos</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Isabelle</namePart>
            <namePart type="family">Augenstein</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Dubrovnik, Croatia</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Syntax is a fundamental component of language, yet few metrics have been employed to capture syntactic similarity or coherence at the utterance- and document-level. The existing standard document-level syntactic similarity metric is computationally expensive and performs inconsistently when faced with syntactically dissimilar documents. To address these challenges, we present FastKASSIM, a metric for utterance- and document-level syntactic similarity which pairs and averages the most similar constituency parse trees between a pair of documents based on tree kernels. FastKASSIM is more robust to syntactic dissimilarities and runs up to to 5.32 times faster than its predecessor over documents in the r/ChangeMyView corpus. FastKASSIM’s improvements allow us to examine hypotheses in two settings with large documents. We find that syntactically similar arguments on r/ChangeMyView tend to be more persuasive, and that syntax is predictive of authorship attribution in the Australian High Court Judgment corpus.</abstract>
    <identifier type="citekey">chen-etal-2023-fastkassim</identifier>
    <identifier type="doi">10.18653/v1/2023.eacl-main.17</identifier>
    <location>
        <url>https://aclanthology.org/2023.eacl-main.17</url>
    </location>
    <part>
        <date>2023-05</date>
        <extent unit="page">
            <start>211</start>
            <end>231</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T FastKASSIM: A Fast Tree Kernel-Based Syntactic Similarity Metric
%A Chen, Maximillian
%A Chen, Caitlyn
%A Yu, Xiao
%A Yu, Zhou
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F chen-etal-2023-fastkassim
%X Syntax is a fundamental component of language, yet few metrics have been employed to capture syntactic similarity or coherence at the utterance- and document-level. The existing standard document-level syntactic similarity metric is computationally expensive and performs inconsistently when faced with syntactically dissimilar documents. To address these challenges, we present FastKASSIM, a metric for utterance- and document-level syntactic similarity which pairs and averages the most similar constituency parse trees between a pair of documents based on tree kernels. FastKASSIM is more robust to syntactic dissimilarities and runs up to to 5.32 times faster than its predecessor over documents in the r/ChangeMyView corpus. FastKASSIM’s improvements allow us to examine hypotheses in two settings with large documents. We find that syntactically similar arguments on r/ChangeMyView tend to be more persuasive, and that syntax is predictive of authorship attribution in the Australian High Court Judgment corpus.
%R 10.18653/v1/2023.eacl-main.17
%U https://aclanthology.org/2023.eacl-main.17
%U https://doi.org/10.18653/v1/2023.eacl-main.17
%P 211-231

Markdown (Informal)

[FastKASSIM: A Fast Tree Kernel-Based Syntactic Similarity Metric](https://aclanthology.org/2023.eacl-main.17) (Chen et al., EACL 2023)

• FastKASSIM: A Fast Tree Kernel-Based Syntactic Similarity Metric (Chen et al., EACL 2023)

ACL

• Maximillian Chen, Caitlyn Chen, Xiao Yu, and Zhou Yu. 2023. FastKASSIM: A Fast Tree Kernel-Based Syntactic Similarity Metric. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 211–231, Dubrovnik, Croatia. Association for Computational Linguistics.