attention机制将整个句子作为输入，从中抽取有用的信息。
每个输出都跟整个句子优化，输出的值为输入的句子的词向量的一个加权求和值。
“This is what attention does, it extracts information from the whole sequence, a weighted sum of all the past encoder states”
https://towardsdatascience.com/attention-is-all-you-need-discovering-the-transformer-paper-73e5ff5e0634
https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/
self-attention:
Self-attention is a sequence-to-sequence operation: a sequence of vectors goes in, and a sequence of vectors comes out. Let’s call the input vectors x1, x2,…, xt and the corresponding output vectors y1, y2,…, yt. The vectors all have dimension k. To produce output vector yi, the self attention operation simply takes a weighted average over all the input vectors, the simplest option is the dot product.
Q, K, V:
Every input vector is used in three different ways in the self-attention mechanism: the Query, the Key and the Value. In every role, it is compared to the other vectors to get its own output yi(Query), to get the j-th output yj(Key) and to compute each output vector once the weights have been established (Value).