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        <title>FastKASSIM: A Fast Tree Kernel-Based Syntactic Similarity Metric</title>
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        <namePart type="given">Maximillian</namePart>
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        <namePart type="family">Chen</namePart>
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    <name type="personal">
        <namePart type="given">Xiao</namePart>
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    <name type="personal">
        <namePart type="given">Zhou</namePart>
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    <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>
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    <identifier type="doi">10.18653/v1/2023.eacl-main.17</identifier>
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%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.