Model Comparison for Semantic Grouping

Francisco Vargas, Kamen Brestnichki, Nils Hammerla

Abstract: We introduce a probabilistic framework for quantifying the semantic similarity between two groups of embeddings. We formulate the task of semantic similarity as a model comparison task in which we contrast a generative model which jointly models two sentences versus one that does not. We illustrate how this framework can be used for the Semantic Textual Similarity tasks using clear assumptions about how the embeddings of words are generated. We apply information criteria based model comparison to overcome the shortcomings of Bayesian model comparison, whilst still penalising model complexity. We achieve competitive results by applying the proposed framework with an appropriate choice of likelihood on the STS datasets.

Keywords: model comparison, semantic similarity, STS, von Mises-Fisher, Information Theoretic Criteria

TL;DR: Competitive alternative to sentence embeddings in the task of semantic similarity using model comparison

Community Implementations: [ 3 code implementations](https://www.catalyzex.com/paper/arxiv:1904.13323/code)