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Semantic Similarity Matching Using Contextualized Representations

Published onJun 08, 2021
Semantic Similarity Matching Using Contextualized Representations


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.

Article ID: 2021S17

Month: May

Year: 2021

Address: Online

Venue: Canadian Conference on Artificial Intelligence

Publisher: Canadian Artificial Intelligence Association


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