What is self attention and how does it work in machine translation?

 Self-attention is a mechanism used in deep learning and natural language processing that enables the model to attend to different parts of the input sequence when making predictions. It allows the model to focus on the most relevant parts of the input sequence for a given output sequence, rather than treating the entire input sequence as a single unit.

In the context of machine translation, self-attention is used in models such as the Transformer architecture, which has achieved state-of-the-art performance in many language tasks. Self-attention is used to represent the context of each word in the input sentence, by assigning a weight to each word in the sentence based on its relevance to the current word being translated.

Here's how self-attention works in machine translation:

1. Input embedding: The input sentence is first tokenized and embedded into a sequence of vectors, one for each word in the sentence.
2. Query, key, and value: For each word in the sentence, three vectors are derived: the query, the key, and the value. These vectors are used to compute the attention weights for each word in the sentence.
3. Attention weights: The attention weights are computed by taking the dot product of the query vector with the key vector for each word in the sentence. The resulting scores are then normalized using a softmax function, which produces a set of weights that sum to 1.
4. Weighted sum: The attention weights are then used to compute a weighted sum of the value vectors, giving a representation of the context for the current word being translated. This allows the model to focus on the most relevant parts of the input sentence for each output word, rather than treating the entire input sequence as a single unit.
5. Multi-head attention: To capture different aspects of the input sequence, self-attention is often performed with multiple sets of query, key, and value vectors, called "heads". The outputs of these different heads are concatenated and passed through a linear layer to produce the final representation of the context for the current output word.

By using self-attention, machine translation models are able to capture the dependencies between different parts of the input sentence, and produce more accurate translations that take into account the context of the entire sentence.