Deep Learning Architectures in Language Learning: A Paradigm for Change

Abstract

Along with the unprecedented developments regarding all dimensions of education systems, the successful implementation of technology-based pedagogic interventions would be of great interest. One of the major areas of progress deals with the essential qualities of the deep neural network in the various domains of research as well as science, which is fundamentally built to imitate the activity of the human brain, including cognitive, affective, social, and emotional factors. Undoubtedly, great attention should be paid to the feasibility of implementation of the varieties of deep learning architectures in language learning environments, which can be assessed. In this paper, deep learning architectures are examined to study the architectures, aspects, and models regarding deep language learning in order to improve the performance of learners of English as a foreign language. The general stages of any dynamic modeling approach include selecting a mathematical model for a physical problem, developing the model, and finally providing its solution to describe the basic components and theoretical background conducing to the emergence of a new paradigm in the context of language learning. Considering the deep learning architectures of Deep Convolution Neural Networks (DCNN), and Recurrent Neural Networks (RNN), aspects of language learning were examined. The survey necessitates implementing a neurophysiological paradigm embracing all learning requirements.