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

Exploring Transformers in Natural Language Generation: GPT, BERT, and XLNet