Semantic Similarity Matching Using Contextualized Representations
by Farhood Farahnak, Elham Mohammadi, MohammadReza Davari, and Leila Kosseim
Published onJun 08, 2021
Semantic Similarity Matching Using Contextualized Representations
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Abstract
Different approaches to address semantic similarity matching generally fall into one of the two categories of interaction-based and representation-based models. While each approach offers its own benefits and can be used in certain scenarios, using a transformer- based model with a completely interaction-based approach may not be practical in many real-life use cases. In this work, we compare the performance and inference time of interaction-based and representation-based models using contextualized representations. We also propose a novel approach which is based on the late interaction of textual representations, thus benefiting from the advantages of both model types.
Creative Commons Attribution 4.0 International License (CC-BY 4.0)