Description
The paper introduces the Transformer, an innovative neural network architecture that relies entirely on attention mechanisms—discarding both recurrence and convolution—delivering faster training and superior performance. Tested on machine translation tasks, the model achieves a remarkable 28.4 BLEU score on English-to-German and sets a new state-of-the-art 41.8 BLEU on English-to-French translation, all while training with significantly less compute time. The authors also demonstrate the model’s versatility by applying it successfully to constituency parsing, showcasing its efficiency, parallelism, and adaptability across diverse tasks.
