Applications of NLG include dialogue systems, advertising, marketing, story generation, and data augmentation. 

NLG Applications 


Large-scale Pre-trained Language Models (PLMs), particularly those transformer-based, make text generation more diverse and fluent. However, because deep neural networks are not readily interpretable, these methods are not guaranteed to be controllable. So, Controllable Text Generation (CTG) using transformer-based PLMs is a trending research area.  

Recent work in NLG 


The complement of Natural Language Understanding (NLU) which aims to produce a normalized representation of the idea expressed in input text (read/understand human language), NLG aims to transform potential representations into specific, self-consistent text, i.e. write in natural language. 

University of California, Berkeley

A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Models

Zhang, H., Song, H., Li, S., Zhou, M., & Song, D. (2022). A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Models. ArXiv, abs/2201.05337.

A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Mode

An area within Natural Language Generation, Controllable Text Generation is integral in developing text generation technologies that are more natural and customizable in practical applications. CTG is the task of generating text given a controlled element. 

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