# FastKASSIM: A Fast Tree Kernel-Based Syntactic Similarity Metric

arXiv: https://arxiv.org/abs/2203.08299 · HTML: https://mlsanity.com/paper/2203.08299/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 66 references that are in the map.
- First posted on arXiv 15 Mar 2022.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Multilingual Constituency Parsing with Self-Attention and Pre-Training](https://mlsanity.com/paper/1812.11760/) (2018)
- [Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussions](https://mlsanity.com/paper/1602.01103/) (2016)
- [Global Explainability of BERT-Based Evaluation Metrics by Disentangling along Linguistic Factors](https://mlsanity.com/paper/2110.04399/) (2021)
- [Weisfeiler-Leman in the BAMBOO: Novel AMR Graph Metrics and a Benchmark for AMR Graph Similarity](https://mlsanity.com/paper/2108.11949/) (2021)
- [Syntax-Enhanced Neural Machine Translation with Syntax-Aware Word Representations](https://mlsanity.com/paper/1905.02878/) (2019)

## Built on by (most cited first, 9 of 9)

- [SUGARCREPE++ Dataset: Vision-Language Model Sensitivity to Semantic and Lexical Alterations](https://mlsanity.com/paper/2406.11171/) (2024)
- [How Good Are LLMs for Literary Translation, Really? Literary Translation Evaluation with Humans and LLMs](https://mlsanity.com/paper/2410.18697/) (2024)
- [PRSA: Prompt Stealing Attacks against Real-World Prompt Services](https://mlsanity.com/paper/2402.19200/) (2024)
- [Syntactic Language Change in English and German: Metrics, Parsers, and Convergences](https://mlsanity.com/paper/2402.11549/) (2024)
- [Returning to the Start: Generating Narratives with Related Endpoints](https://mlsanity.com/paper/2404.00829/) (2024)
- [The Fewer Splits are Better: Deconstructing Readability in Sentence Splitting](https://mlsanity.com/paper/2302.00937/) (2023)
- [Automatically Suggesting Diverse Example Sentences for L2 Japanese Learners Using Pre-Trained Language Models](https://mlsanity.com/paper/2506.03580/) (2025)
- [Enhancing Automatic Term Extraction with Large Language Models via Syntactic Retrieval](https://mlsanity.com/paper/2506.21222/) (2025)
- [A General Kernel Framework for Non-CND Distance Measures Using |D|-Dimensional Sparse Landmark Embeddings](https://mlsanity.com/paper/2609.19083/) (2026)

## Abstract (arXiv)

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.