Semantic similarity controllers: On the trade-off between accuracy and interpretability

In recent times, we have seen an explosion in the number of new solutions to address the problem of semantic similarity. In this context, solutions of a neuronal nature seem to obtain the best results. However, there are some problems related to their low interpretability as well as the large number of resources needed for their training. In this work, we focus on the data-driven approach for the design of semantic similarity controllers. The goal is to offer the human operator a set of solutions in the form of a Pareto front that allows choosing the configuration that best suits a specific use case. To do that, we have explored the use of multi-objective evolutionary algorithms that can help find break-even points for the problem of accuracy versus interpretability. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This work focuses on the data-driven approach for designing semantic similarity controllers, aiming to offer a set of solutions to human operators in the form of a Pareto front for choosing the best configuration for a specific use case. Multi-objective evolutionary algorithms are explored to find trade-offs between accuracy and interpretability.