The paper proposes a new type of neural network called the "Transformer," which uses only attention mechanisms (instead of recurrence and convolution) to process input sequences. The authors tested this model on two machine translation tasks and found that it performed better than previous models, was faster to train, and required less computing power. Specifically, their model achieved a BLEU score of 28.4 on the English-to-German translation task and 41.8 on the English-to-French translation task, outperforming previous results by a significant margin. They also showed that the Transformer can be applied to other tasks such as English constituency parsing.
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