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Syntax is a fundamental component of language, yet few metrics have beenemployed to capture syntactic similarity or coherence at the utterance- anddocument-level. The existing standard document-level syntactic similaritymetric is computationally expensive and performs inconsistently when faced withsyntactically dissimilar documents. To address these challenges, we presentFastKASSIM, a metric for utterance- and document-level syntactic similaritywhich pairs and averages the most similar constituency parse trees between apair of documents based on tree kernels. FastKASSIM is more robust to syntacticdissimilarities and runs up to to 5.32 times faster than its predecessor overdocuments in the r/ChangeMyView corpus. FastKASSIM's improvements allow us toexamine hypotheses in two settings with large documents. We find thatsyntactically similar arguments on r/ChangeMyView tend to be more persuasive,and that syntax is predictive of authorship attribution in the Australian HighCourt Judgment corpus.

FastKASSIM: A Fast Tree Kernel-Based Syntactic Similarity Metric