Editing Attention is All You Need (section)

== Performance ==
The paper evaluated the Transformer on two machine translation tasks: WMT 2014 English-to-German and WMT 2014 English-to-French. The paper used a new training regime called label smoothing to prevent [[overfitting]] and improve generalization. The paper also used beam search with a length penalty to generate outputs. The paper reported that the Transformer achieved 28.4 BLEU on English-to-German and 41.8 BLEU on English-to-French, surpassing the previous best results by over 2 BLEU and setting new single-model state-of-the-art scores. The paper also showed that the Transformer was more efficient and faster than recurrent or convolutional models, requiring significantly less time to train and infer.

The paper demonstrated that attention is indeed all you need for sequence transduction, as it can capture complex dependencies and context information without recurrence or convolution. The paper also showed that attention can be scaled up to large models and data sets, achieving impressive results on challenging tasks.