Natural Language Generation in AI: Developing Human-Like Text Through Deep Learning

Natural Language Generation (NLG) is a subset of artificial intelligence that has as its goal to develop techniques to synthesize human language. This paper focuses on the finding of NLG as a field of study as well as how it benefitted from ideas such as deep learning. Next, we consider the development of NLG models, from standard Recurrent Neural Networks to Long Short-Term Memory networks and from Transformers like GPT. Transformer models have proven to be magnificent for the NLG as they remove the drawbacks of previous models and have improved the long-range dependencies and have synthesized appropriate topic-based content. This paper assesses the merits and the demerit of these models and the areas where that they are used such as healthcare, content creation and customer care. Transformer models are particularly good at producing high-quality text, for which it comes with severe difficulties though. The research for this paper aims to consider future developments of NLG with regards to scalability and the potential for change across fields.