Improving Healthcare Outcomes with Learning Models for Machine Commonsense Reasoning Systems

Machine commonsense reasoning (MCR) systems can significantly improve the way we interact with machines. MCR systems are therefore an important element in any human-centric applications. Recent advances in machine learning (ML) have enabled breakthroughs in MCR technologies. This paper aims to improve healthcare outcomes by making human-machine interactions more intuitive than before. It presents learning models developed for MCR. Specifically, it presents a critical analysis of state-of-the-art deep learning (DL) models for MCR. These include recurrent neural network (RNN), transfer learning (TL), and transformers. Transformers, in particular, have been found to be effective for a range of natural language processing (NLP) applications, including MCR. Based on the analysis, another contribution of this paper is to assemble useful MCR tools into an adaptable MCR toolbox. To ensure broad applicability, the toolbox can be customizable for different MCR applications. Our research focuses on two specific MCR applications: commonsense validation and commonsense explanation. The former concerns identifying statements that do not make commonsense. The latter aims at explaining the reason why a given statement does not make commonsense. The paper presents some preliminary results of applying elements of the assembled toolbox to the two MCR applications. These results indicate that it is possible to achieve near human performances using finely-tuned state-of-the-art DL methods for the two MCR applications.