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 training time. The core idea is using self-attention to relate different positions of the input sequence to compute a representation of the entire sequence.