Attention is All You Need

This paper introduces the Transformer, a novel neural network architecture based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. It demonstrates that this architecture can be trained more efficiently and achieve state-of-the-art results on machine translation tasks. The key idea is to weigh the importance of different parts of the input sequence when processing each element, allowing for parallelization and capturing long-range dependencies more effectively than previous recurrent or convolutional models. The Transformer has since become a foundational model in many NLP tasks, including text generation, translation, and video processing.