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 attention mechanisms alone can achieve state-of-the-art results in machine translation tasks, while also being more parallelizable and requiring less training time compared to recurrent models. The core idea is to weigh the importance of different parts of the input sequence when processing it, allowing the model to capture long-range dependencies more effectively.