CNN-LSTN-Attention

CNN-LSTN-Attention is a neural network architecture used in natural language processing tasks such as machine translation, text summarization, and sentiment analysis. It combines the strengths of Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and attention mechanisms.

CNNs are good at capturing local patterns in input sequences, while LSTMs are good at modeling long-term dependencies. Attention mechanisms allow the network to selectively focus on certain parts of the input sequence, improving performance.

In the CNN-LSTN-Attention architecture, the input sequence is first processed by a CNN to extract local features. The resulting feature maps are then fed into an LSTM network to capture long-term dependencies. Finally, an attention mechanism is applied to the LSTM output to selectively weigh the importance of different parts of the input sequence.

This architecture has shown promising results in various natural language processing tasks, outperforming other state-of-the-art models.