Self-attention modules
Self-attention modules became popular with the introduction of an NLP model known as the Transformer. In NLP applications such as language translation, the model often needs to read sentences word by word to understand them before producing the output. The neural network used prior to the advent of the Transformer was some variant on the recurrent neural network (RNN), such as long short-term memory (LSTM). The RNN has internal states to remember words as it reads a sentence.
One drawback of that is that when the number of words increases, the gradients for the first words vanish. That is to say, the words at start of the sentence become less important gradually as the RNN reads more words.
The Transformer does things differently. It reads all the words at once and weights the importance of each individual word. Therefore, more attention is given to words that are more important, and hence the name attention. Self-attention is a cornerstone of state-of...
