Perplexity—The Attention mechanism for LSTM networks is a way to free the encoder-decoder architecture from the fixed-length internal representation. It does this by keeping the intermediate outputs from the encoder LSTM from each step of the input sequence and training the model to learn to pay selective attention to these inputs and relate them to items. The Attention mechanism allows the neural network to cope with long sentences, especially those that are longer than the sentences in the training corpus. As the popularity of attention in machine learning grows, so does the list of neural architectures that incorporate an attention mechanism. If you want to learn more about the Attention mechanism, there are many resources available online that provide an intuitive understanding of how it works.
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