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 model can be trained faster and achieves state-of-the-art results in machine translation tasks. The core innovation is the multi-headed self-attention mechanism, which allows the model to weigh the importance of different parts of the input sequence when processing it, leading to better performance in capturing long-range dependencies.