Self-Attention Mechanism
Explore how the self-attention mechanism enables transformer models to capture relationships between words in a sentence. Understand embedding representations and how query, key, and value matrices are used to compute attention, helping models interpret context accurately in NLP tasks.
To understand how multi-head attention works, we first need to understand the self-attention mechanism.
Self-attention mechanism
Let's understand the self-attention mechanism with an example. Consider the following sentence:
In the preceding sentence, the pronoun 'it' could mean either 'dog' or 'food'. By reading the sentence, we can easily understand that the pronoun 'it' implies the 'dog' and not 'food'. But how does our model understand that in the given sentence, the pronoun 'it' implies the 'dog' and not 'food'? Here is where the self-attention mechanism helps us.
Representation of the words
In the given sentence, ‘A dog ate the food because it was hungry’, first our model computes the representation of the word ‘A’, next it computes the representation of the word ‘dog’, then it computes the representation of the word ‘ate’, and so on. While computing the representation of each word, it relates each word to all other words in the sentence to understand more about the word.
