Attention and self-attention are both mechanisms in neural networks that allow the model to focus on specific parts of an input sequence. However, they differ in how they are used.
Attention is typically used to relate an input sequence to an output sequence. For example, in machine translation, attention can be used to help the model translate a sentence from one language to another by focusing on the most relevant words in the input sentence.
Self-attention, on the other hand, is used to relate different parts of a single input sequence to each other. This can be useful for tasks such as natural language understanding, where the model needs to understand the relationships between different words in a sentence.
Here is an example of how attention works:
Consider the task of machine translation. The model is given an input sentence in one language, and it needs to output the corresponding sentence in another language. The model can use attention to help it translate the sentence by focusing on the most relevant words in the input sentence. For example, if the input sentence is “I like apples,” the model might use attention to focus on the word “apples” when translating the sentence to French. This would help the model to generate the correct translation, “J’aime les pommes.”
Here is an example of how self-attention works:
Consider the task of natural language understanding. The model is given a sentence, and it needs to understand the meaning of the sentence. The model can use self-attention to help it understand the meaning of the sentence by focusing on the relationships between different words in the sentence. For example, if the sentence is “The cat sat on the mat,” the model might use self-attention to focus on the relationship between the words “cat” and “mat.” This would help the model to understand that the cat is sitting on the mat.
In general, attention is a more general mechanism than self-attention. Attention can be used to relate any two sequences to each other, while self-attention can only be used to relate different parts of a single sequence to each other. However, self-attention is often more useful for tasks such as natural language understanding, where the model needs to understand the relationships between different words in a sentence.
See Also: Attention Weights