Although the long short-term memory network LSTM has made significant progress in processing sequence data, as the length of the sequence increases, the computational complexity and training time also increase accordingly. In order to solve these problems, researchers proposed the Attention Mechanism.
The attention mechanism was proposed by Bahdanau et al. in 2014. The method is inspired by the visual attention of humans when reading text. When reading, people tend to only focus on the parts related to panama mobile database the current task and ignore other irrelevant content. The introduction of this mechanism allows the neural network to automatically focus on important parts when processing long sequences, thereby improving computational efficiency and accuracy.
A classic application scenario is neural machine translation NMT. In the original NMT model, the decoder needs to generate text in the target language based on a fixed-size context vector generated by the encoder. This method encounters problems of information loss and gradient disappearance when processing long texts. After the introduction of the attention mechanism, the decoder can assign different weights to all hidden states of the encoder output as needed at each time step, thereby better capturing the long-distance dependencies in the input sequence. This improvement has significantly improved the translation quality.