self-attention+MLP

Self-attention MLP (Multi-Layer Perceptron) is a type of neural network architecture used in natural language processing tasks such as language translation, text classification, and sentiment analysis. It is based on the concept of self-attention, which allows the model to focus on different parts of the input sequence to extract relevant information.

In self-attention MLP, the input sequence is first transformed into a set of key, query, and value vectors. These vectors are then used to compute attention scores, which determine the importance of each element in the input sequence. The attention scores are then used to weight the value vectors, which are combined to obtain the final output.

The MLP component of self-attention MLP is used to transform the input vectors into a higher-dimensional space, allowing the model to capture more complex relationships between the input elements. Overall, self-attention MLP has shown to be an effective approach for a wide range of natural language processing tasks.