Introduced by Bhdanau et al. in Neural Machine Translation by Jointly Learning to Align and Translate.
Additive Attention, also known as Bahdanau Attention, uses a one-hidden layer feed-forward network to calculate the attention alignment score:
where Va and Wa are learned attention parameters. Here h refers to the hidden states for the encoder, and s is the hidden states for the decoder. The function above is thus a type of alignment score function. We can use a matrix of alignment scores to show the correlation between source and target words, as the Figure to the right shows.
Within a neural network, once we have the alignment scores, we calculate the final scores using a softmax function of these alignment scores (ensuring it sums to 1).
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