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Self-attention is a mechanism that allows a model to focus on different parts of its input sequence. It is a key component of the Transformer architecture, which is a state-of-the-art model for natural language processing (NLP) tasks such as machine translation, text summarization, and question answering.

How self-attention works

Self-attention works by calculating a similarity score between each word in the input sequence and every other word in the sequence. This similarity score is then used to create a weighted average of the input sequence, where the weights are determined by the similarity scores. This weighted average is then used as the output of the self-attention layer.

Example

Consider the following input sentence:

I love natural language processing.

The self-attention layer will calculate a similarity score between each word in the sentence and every other word in the sentence. For example, the similarity score between the word “love” and the word “natural” will be high, because these two words are semantically related.

The self-attention layer will then use the similarity scores to create a weighted average of the input sentence. The weights will be higher for words that have higher similarity scores. For example, the weight for the word “natural” will be higher than the weight for the word “processing”, because the word “natural” has a higher similarity score with the word “love”.

To calculate self-attention in the encoder, the model first creates three vectors for each word in the input sequence:

• A query vector
• A key vector
• A value vector

The query vector represents the model’s current focus of attention. The key vector represents the importance of each word in the input sequence. And the value vector represents the information that the model wants to extract from each word.

If the query vector for the word “I” will represent the model’s current focus of attention. The key vector for the word “love” will represent the importance of the word “love” in the input sequence. And the value vector for the word “natural” will represent the information that the model wants to extract from the word “natural”.

The model will then calculate a similarity score between the query vector for the word “I” and each of the key vectors for the other words in the sentence. This similarity score will be high for words that are semantically related to the word “I”, such as the word “love”.

The model will then use the similarity scores to create a weighted average of the value vectors for the other words in the sentence. The weights will be higher for words that have higher similarity scores.

The weighted average of the value vectors will then be used as the output of the self-attention layer for the word “I”. In other words, the self-attention layer will produce a representation of the input sentence that focuses on the most important words in the sentence, based on their relationships to each other.

Benefits of self-attention in the encoder

Self-attention has a number of benefits for the encoder of a Transformer model. First, it allows the model to learn long-range dependencies in the input sequence. This is important for tasks such as machine translation, where the model needs to understand the relationship between words that are far apart in the sentence.

Second, self-attention allows the model to focus on the most important parts of the input sequence. This is important for tasks such as text summarization and question answering, where the model needs to extract the key information from the input text.

Self-attention is a powerful mechanism that can be used to improve the performance of NLP models. It is a key component of the Transformer architecture, which is a state-of-the-art model for many NLP tasks.