给定一段文本，为其生成摘要。


This paper proposes a new neural network architecture called the Transformer
that addresses these problems. The Transformer, instead of relying on recurrence,
relies entirely on an attention mechanism to draw global dependencies between
input and output. The Transformer allows for significantly more parallelization
than recurrent models because it does not require ordered inputs and can process
all inputs and outputs in parallel. 本篇论文提出了一种新的神经网络结构，称为Transformer，旨在解决上述问题。与依赖于循环的模型不同的是，Transformer完全依赖于一种注意力机制来建立输入和输出之间的全局依赖关系。相比循环模型，Transformer允许更高程度的并行化，因为它不需要有序的输入，并且可以同时处理所有的输入和输出。
The Transformer is a new neural network architecture that relies on an attention mechanism to draw global dependencies between input and output. It allows for significantly more parallelization than recurrent models and does not require ordered inputs.