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 significantly outperform existing models in machine translation tasks while being more parallelizable and requiring 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 contextual understanding and improved performance.