大体思想和RNN encoder-decoder是一样的，只是用来LSTM来实现。
paper提到三个important point：
1）encoder和decoder的LSTM是两个不同的模型
2）deep LSTM表现比shallow好，选用了4层的LSTM
3）实践中发现将输入句子reverse后再进行训练效果更好。So for example, instead of mapping the sentence a,b,c to the sentence α,β,γ, the LSTM is asked to map c,b,a to α,β,γ, where α, β, γ is the translation of a, b, c. This way, a is in close proximity to α, b is fairly close to β, and so on, a fact that makes it easy for SGD to “establish communication” between the input and the output.
