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 achieve state-of-the-art results in machine translation tasks while being more parallelizable and requiring significantly less time to train. 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 understanding of context and relationships.