A Systematic Literature Review on Text Generation using Deep Neural Networks

Abstract

Text generation using deep neural networks has become an exciting area of research. Deep neural networks, such as recurrent neural networks (RNNs) and transformers, have shown remarkable capabilities in generating coherent and contextually relevant text. By training these models on large text corpora, they learn to capture the underlying patterns and structures of the language. This enables them to generate new text that resembles the style and content of the training data. Text generation using deep neural networks has a wide range of applications, including chatbots, language translation, poetry generation, and even code generation.