lstm attention

LSTM attention is a technique used in natural language processing (NLP) and deep learning. LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN) that is capable of learning long-term dependencies in sequential data. Attention is a mechanism that allows the model to selectively focus on certain parts of the input when making predictions.

LSTM attention combines the power of LSTM and attention to improve the accuracy of NLP models. In this technique, the LSTM model is augmented with an attention mechanism that allows it to selectively focus on certain parts of the input sequence. The attention mechanism assigns weights to each input element based on its importance to the output. These weights are used to calculate a weighted sum of the input elements, which is then fed into the LSTM model.

The use of LSTM attention has been shown to improve the performance of various NLP tasks such as language translation, sentiment analysis, and question answering.