The survey: Text generation models in deep learning

Abstract

Deep learning methods possess many processing layers to understand the stratified representation of data and have achieved state-of-art results in several domains. Recently, deep learning model designs and architectures have unfolded in the context of Natural Language Processing (NLP). This survey presents a brief description of the advances that have occurred in the area of Deep Generative modeling. This work considers most of the papers from 2015 onwards. In this paper, we review many deep learning models that have been used for the generation of text. We also summarize the various models and have put forward the detailed understanding of past, present, and future of text generation models in deep learning. Furthermore, DL approaches that have been explored and evaluated in different application domains in NLP are included in this survey.

Keywords

• Natural Language Processing (NLP)
• Deep learning
• Word embeddings
• Recurrent Neural Networks (RNNs)
• Convolutional Neural Networks (CNNs)
• Variational Auto-Encoders (VAEs)