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 achieves state-of-the-art results in machine translation tasks while being more parallelizable and requiring significantly less training time. The core innovation is the '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 and understanding of context.