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 in machine translation tasks. The key innovation is the self-attention mechanism, which allows the model to weigh the importance of different parts of the input sequence when processing each element, enabling parallelization and capturing long-range dependencies more effectively.