What is exactly the attention mechanism introduced to RNN (recurrent neural network)? It would be nice if you could make it easy to understand!

The attention mechanism introduced to Recurrent Neural Networks (RNNs) is a technique that allows the model to focus on specific parts of the input sequence when generating the output. Here’s a simplified explanation:

Key Components

• Encoder and Decoder: In an RNN with an attention mechanism, the model typically consists of an encoder and a decoder. The encoder processes the input sequence and generates a set of output vectors (e.g., (h_1, h_2, h_3, h_4)), while the decoder generates the output sequence based on these vectors124.

How Attention Works

• Attention Weights: The attention mechanism calculates weights ((\alpha_{ij})) that represent the importance of each input element (encoded as (h_j)) when predicting the next output element. These weights are computed based on the current state of the decoder ((s_i)) and the encoder's output vectors ((h_j))145.

Process

1. Encoder Output: The encoder processes the input sequence and produces a set of hidden state vectors ((h_1, h_2, h_3, h_4))1.
2. Attention Calculation: At each time step of the decoder, the attention mechanism computes the attention weights ((\alpha_{ij})) by comparing the decoder's current state ((s_i)) with each of the encoder's output vectors ((h_j)). This is typically done using a fully connected network that learns to assign these weights14.
3. Context Vector: The attention weights are then used to compute a context vector ((c_i)) which is a weighted sum of the encoder's output vectors. This context vector captures the relevant information from the input sequence for the current output prediction135.
4. Decoder Output: The decoder uses the context vector ((c_i)) along with its current state ((s_i)) to generate the next output element ((y_i))1.

Benefits

• Focus on Relevant Parts: The attention mechanism allows the model to focus on the most relevant parts of the input sequence at each time step, rather than relying on a fixed-length vector that might lose information over long sequences34.
• Improved Performance: This leads to better performance in tasks like machine translation, text summarization, and image captioning, where understanding the context and long-range dependencies is crucial235.

In essence, the attention mechanism mimics human attention by selectively focusing on the most important parts of the input data, which enhances the model's ability to handle complex and long sequences effectively.