FastKASSIM: A Fast Tree Kernel-Based Syntactic Similarity Metric

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 dependency parse trees between a pair of documents based on tree kernels. FastKAS-SIM is more robust to syntactic dissimilarities and differences in length, and runs up to to 5.2 times faster than our baseline method over the documents in the r/ChangeMyView corpus. * denotes equal contribution.Utterance 1: When we hate, we always move away from the grace of God. When we become resentful and unforgiving, the world around us seems spiteful and meaningless. Utterance 2: How can you be skiing if you are already swimming? FastKASSIM Score: 0.219 CASSIM Score: 0.838 LSM Score: 0.623 Utterance 1: I like swimming because it is cool. Utterance 2: I love running because it is fun.

Does splitting make sentence easier?

“…SUBSET and SUBTREE are both measures based on the idea of Tree Kernel (Collins and Duffy, 2002;Moschitti, 2006;Chen et al, 2022). The former considers how many subgraphs two parses share, while the latter considers how many subtrees.…”