summarize in simple language keeping all relevant details intact: We parametrize the alignment model a as a feedforward neural network which is jointly trained with
all the other components of the proposed system. Note that unlike in traditional machine translation,

the alignment is not considered to be a latent variable. Instead, the alignment model directly computes a soft alignment, which allows the gradient of the cost function to be backpropagated through.
This gradient can be used to train the alignment model as well as the whole translation model jointly.
We can understand the approach of taking a weighted sum of all the annotations as computing an
expected annotation, where the expectation is over possible alignments. Let αij be a probability that the target word is aligned to, or translated from, a source word xj . Then, the i-th context vector
is the expected annotation over all the annotations with probabilities αij .The probability αij , or its associated energy eij , reflects the importance of the annotation hj with
respect to the previous hidden state si−1 in deciding the next state si and generating yi
. Intuitively, this implements a mechanism of attention in the decoder. The decoder decides parts of the source sentence to pay attention to. By letting the decoder have an attention mechanism, we relieve the encoder from the burden of having to encode all information in the source sentence into a fixedlength vector. With this new approach the information can be spread throughout the sequence of annotations, which can be selectively retrieved by the decoder accordingly. Your answer should have exactly 7 paragraphs. Make sure that the last paragraph starts with the word "accounts" and that each paragraph is separated by two new lines.
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