The Latest in Natural Language Generation: Trends, Tools and Applications in Industry

Abstract

Natural Language Generation (NLG) has experienced rapid progress in recent years with advancements in artificial intelligence contributing to its evolution. In this paper, we present a comprehensive review of the latest trends, models, tools, and applications of NLG across various industries. We discuss the increasing use of deep learning algorithms and neural networks, the development of multilingual NLG models, and the integration of NLG with other artificial intelligence (AI) technologies such as natural language understanding (NLU) and machine translation (MT). Furthermore, we examine the different pre-trained language models available, including autoregressive models, masked language models, encoder-decoder models, and hybrid models, along with their evaluation and improvement. We also explore the applications of NLG in business intelligence, customer service, healthcare, education, and multimodal language models, highlighting the potential of NLG in communication and decision-making, as well as its significant implications for cybersecurity. This paper aims to provide a thorough understanding of the current state of NLG and its potential to revolutionize various industries in the digital era.