Ref: https://arxiv.org/abs/1706.03762

This classic research paper introduces the Transformer, a novel neural network architecture for sequence transduction tasks like machine translation. Unlike previous models relying on recurrent or convolutional layers, the Transformer uses solely attention mechanisms, enabling greater parallelization and faster training. Experiments demonstrate its superior performance on English-to-German and English-to-French translation, achieving state-of-the-art results with significantly reduced training costs. Furthermore, the Transformer's effectiveness extends to other tasks, as shown by its successful application to English constituency parsing. The paper details the Transformer's architecture, including multi-head attention and positional encoding, and analyzes its advantages over existing methods.