Continuous learning mechanism of NLU-ML models boosted by human feedback

Abstract

In this paper, we propose a novel framework that enables a machine learning model to constantly learn over a period of time and hence improve the performance with time and more data. We have compared the performance of different models which were trained only on the actual data against models trained with the data aided by the feedback collected by the automated framework.