A Survey of Text Classification With Transformers: How Wide? How Large? How Long? How Accurate? How Expensive? How Safe?

Text classification is a basic task in natural language processing (NLP) with applications from sentiment analysis to question-answering with chat bots. In recent years, transformer-based models have emerged as the prevailing framework in NLP, demonstrating excellent results across many benchmarks. This paper recommends an expanded taxonomy of applications and provides a review of the performance of different models across these applications. The use of traditional research techniques plus co-citation and bibliographic coupling provides a comprehensive view of the current and past research in this area. The study begins by providing an overview of the history of transformer-based models with an emphasis on…