  Recent years have seen a proliferation of attention mechanisms and the rise
of Transformers in Natural Language Generation (NLG). Previously,
state-of-the-art NLG architectures such as RNN and LSTM ran into vanishing
gradient problems; as sentences grew larger, distance between positions
remained linear, and sequential computation hindered parallelization since
sentences were processed word by word. Transformers usher in a new era. In this
paper, we explore three major Transformer-based models, namely GPT, BERT, and
XLNet, that carry significant implications for the field. NLG is a burgeoning
area that is now bolstered with rapid developments in attention mechanisms.
From poetry generation to summarization, text generation derives benefit as
Transformer-based language models achieve groundbreaking results.
