How can I efficiently implement an attention mechanism to generate context vectors at each decoder step using an LSTM in a sequence-to-sequence model

Question

With the help of code can you explain to me How can I efficiently implement an attention mechanism to generate context vectors at each decoder step using an LSTM in a sequence-to-sequence model?

Answer 1

An attention mechanism efficiently generates context vectors at each decoder step in an LSTM-based sequence-to-sequence model by computing dynamic alignment scores between the decoder's current state and all encoder outputs, then applying a weighted sum to focus on relevant information.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• Uses an LSTM-based Encoder to generate a sequence of hidden states.
• Uses an LSTM-based Decoder initialized with encoder states.
• Applies an Attention Mechanism at each decoder step for dynamic focus.
• Computes Context Vectors by weighting encoder outputs per decoder timestep.
• Concatenates Attention and Decoder Outputs for better sequence generation.

Hence, incorporating an attention mechanism in an LSTM sequence-to-sequence model enables the decoder to dynamically extract relevant information, improving sequence generation accuracy.