Learning Attention in Deep Neural Networks for Natural Language Processing

In this article, we propose a new architecture called Bridge that enhances the efficiency and accuracy of sequential data processing using multi-head attention. The Bridge model is designed to address two main limitations of existing attention mechanisms: computational cost and the need for parallelization.
To overcome these challenges, Bridge employs a novel encoder-decoder structure with stacked self-attention layers and fully connected feed-forward networks. The multi-head attention mechanism in Bridge allows it to learn multiple representation subspaces simultaneously, which improves the ability of the model to capture complex patterns in sequential data.
The key innovation of Bridge is the use of a residual connection that enables the model to learn more complex mappings between input and output sequences. This approach reduces the computational cost of attention while maintaining its accuracy. Additionally, Bridge uses normalization techniques around each sub-layer to stabilize the training process and improve generalization.
Another important aspect of Bridge is its ability to adapt to different sequential data processing tasks by adjusting the number of stacked attention layers. This flexibility allows Bridge to be applied to various applications, including language modeling, machine translation, and speech recognition.
In summary, Bridge is a powerful and efficient architecture that leverages multi-head attention to improve the accuracy and efficiency of sequential data processing. Its innovative use of residual connections and normalization techniques makes it an exciting addition to the field of natural language processing and beyond.