Chapter 6: Self-Attention and Multi-Head Attention in Transformers

This chapter discusses the self-attention mechanism, which is widely used in natural language processing tasks. While the self-attention mechanism is highly effective, there are certain limitations that have been identified by researchers over the years. In order to overcome these limitations and adapt the mechanism to specific tasks, several variants and extensions have been developed.

One such variant is the local attention mechanism, which focuses on a smaller subset of the input sequence as opposed to the entire sequence. Another variant is the global attention mechanism, which takes into account the entire input sequence to compute the attention weights. In addition to these, there are several other variants and extensions that have been proposed and studied in the literature.

Therefore, it is important for researchers and practitioners to be aware of the various forms of attention mechanisms and their applications in natural language processing tasks.

Local Attention

Unlike self-attention, which attends to all positions of the sequence, local attention restricts the attention to only a subset of the positions. This can be more efficient and sometimes even more effective, particularly in tasks where the input naturally has a local structure.

One example of a task where local attention might be useful is image classification. In an image, each pixel can be thought of as a position in the sequence, and nearby pixels are likely to be more relevant to each other than distant pixels. By using local attention, a model can focus on the relevant pixels and ignore the irrelevant ones, leading to better performance.

Another example is natural language processing, where local attention can be used to capture the relationships between words in a sentence. If two words are nearby in the sentence, they are more likely to be related than if they are far apart. By using local attention, a model can focus on the relevant words and ignore the irrelevant ones, leading to better performance.

Overall, local attention is a powerful tool for machine learning models, allowing them to efficiently capture the relevant parts of the input and improve their performance on a variety of tasks.

Global Attention

In contrast to local attention, global attention is where one of the input positions is allowed to attend to all other positions. This is especially useful in tasks like machine translation, where alignment between certain positions is important. Global attention essentially allows the model to consider the entire input sequence when making a prediction, rather than only a limited window of positions.

This can be particularly helpful when dealing with long sequences, as it allows the model to capture dependencies that may not be apparent from just a local window. Additionally, global attention can also help to address mismatches between the input and output sequences, since the model can more easily adjust its focus to the relevant positions in the input. Overall, global attention can greatly improve the performance of neural models in a variety of natural language processing tasks.

This chapter discusses the self-attention mechanism, which is widely used in natural language processing tasks. While the self-attention mechanism is highly effective, there are certain limitations that have been identified by researchers over the years. In order to overcome these limitations and adapt the mechanism to specific tasks, several variants and extensions have been developed.

One such variant is the local attention mechanism, which focuses on a smaller subset of the input sequence as opposed to the entire sequence. Another variant is the global attention mechanism, which takes into account the entire input sequence to compute the attention weights. In addition to these, there are several other variants and extensions that have been proposed and studied in the literature.

Therefore, it is important for researchers and practitioners to be aware of the various forms of attention mechanisms and their applications in natural language processing tasks.

Local Attention

Unlike self-attention, which attends to all positions of the sequence, local attention restricts the attention to only a subset of the positions. This can be more efficient and sometimes even more effective, particularly in tasks where the input naturally has a local structure.

One example of a task where local attention might be useful is image classification. In an image, each pixel can be thought of as a position in the sequence, and nearby pixels are likely to be more relevant to each other than distant pixels. By using local attention, a model can focus on the relevant pixels and ignore the irrelevant ones, leading to better performance.

Another example is natural language processing, where local attention can be used to capture the relationships between words in a sentence. If two words are nearby in the sentence, they are more likely to be related than if they are far apart. By using local attention, a model can focus on the relevant words and ignore the irrelevant ones, leading to better performance.

Overall, local attention is a powerful tool for machine learning models, allowing them to efficiently capture the relevant parts of the input and improve their performance on a variety of tasks.

Global Attention

In contrast to local attention, global attention is where one of the input positions is allowed to attend to all other positions. This is especially useful in tasks like machine translation, where alignment between certain positions is important. Global attention essentially allows the model to consider the entire input sequence when making a prediction, rather than only a limited window of positions.

This can be particularly helpful when dealing with long sequences, as it allows the model to capture dependencies that may not be apparent from just a local window. Additionally, global attention can also help to address mismatches between the input and output sequences, since the model can more easily adjust its focus to the relevant positions in the input. Overall, global attention can greatly improve the performance of neural models in a variety of natural language processing tasks.

This chapter discusses the self-attention mechanism, which is widely used in natural language processing tasks. While the self-attention mechanism is highly effective, there are certain limitations that have been identified by researchers over the years. In order to overcome these limitations and adapt the mechanism to specific tasks, several variants and extensions have been developed.

One such variant is the local attention mechanism, which focuses on a smaller subset of the input sequence as opposed to the entire sequence. Another variant is the global attention mechanism, which takes into account the entire input sequence to compute the attention weights. In addition to these, there are several other variants and extensions that have been proposed and studied in the literature.

Therefore, it is important for researchers and practitioners to be aware of the various forms of attention mechanisms and their applications in natural language processing tasks.

Local Attention

Unlike self-attention, which attends to all positions of the sequence, local attention restricts the attention to only a subset of the positions. This can be more efficient and sometimes even more effective, particularly in tasks where the input naturally has a local structure.

One example of a task where local attention might be useful is image classification. In an image, each pixel can be thought of as a position in the sequence, and nearby pixels are likely to be more relevant to each other than distant pixels. By using local attention, a model can focus on the relevant pixels and ignore the irrelevant ones, leading to better performance.

Another example is natural language processing, where local attention can be used to capture the relationships between words in a sentence. If two words are nearby in the sentence, they are more likely to be related than if they are far apart. By using local attention, a model can focus on the relevant words and ignore the irrelevant ones, leading to better performance.

Overall, local attention is a powerful tool for machine learning models, allowing them to efficiently capture the relevant parts of the input and improve their performance on a variety of tasks.

Global Attention

In contrast to local attention, global attention is where one of the input positions is allowed to attend to all other positions. This is especially useful in tasks like machine translation, where alignment between certain positions is important. Global attention essentially allows the model to consider the entire input sequence when making a prediction, rather than only a limited window of positions.

This can be particularly helpful when dealing with long sequences, as it allows the model to capture dependencies that may not be apparent from just a local window. Additionally, global attention can also help to address mismatches between the input and output sequences, since the model can more easily adjust its focus to the relevant positions in the input. Overall, global attention can greatly improve the performance of neural models in a variety of natural language processing tasks.

This chapter discusses the self-attention mechanism, which is widely used in natural language processing tasks. While the self-attention mechanism is highly effective, there are certain limitations that have been identified by researchers over the years. In order to overcome these limitations and adapt the mechanism to specific tasks, several variants and extensions have been developed.

One such variant is the local attention mechanism, which focuses on a smaller subset of the input sequence as opposed to the entire sequence. Another variant is the global attention mechanism, which takes into account the entire input sequence to compute the attention weights. In addition to these, there are several other variants and extensions that have been proposed and studied in the literature.

Therefore, it is important for researchers and practitioners to be aware of the various forms of attention mechanisms and their applications in natural language processing tasks.

Local Attention

Unlike self-attention, which attends to all positions of the sequence, local attention restricts the attention to only a subset of the positions. This can be more efficient and sometimes even more effective, particularly in tasks where the input naturally has a local structure.

One example of a task where local attention might be useful is image classification. In an image, each pixel can be thought of as a position in the sequence, and nearby pixels are likely to be more relevant to each other than distant pixels. By using local attention, a model can focus on the relevant pixels and ignore the irrelevant ones, leading to better performance.

Another example is natural language processing, where local attention can be used to capture the relationships between words in a sentence. If two words are nearby in the sentence, they are more likely to be related than if they are far apart. By using local attention, a model can focus on the relevant words and ignore the irrelevant ones, leading to better performance.

Overall, local attention is a powerful tool for machine learning models, allowing them to efficiently capture the relevant parts of the input and improve their performance on a variety of tasks.

Global Attention

In contrast to local attention, global attention is where one of the input positions is allowed to attend to all other positions. This is especially useful in tasks like machine translation, where alignment between certain positions is important. Global attention essentially allows the model to consider the entire input sequence when making a prediction, rather than only a limited window of positions.

This can be particularly helpful when dealing with long sequences, as it allows the model to capture dependencies that may not be apparent from just a local window. Additionally, global attention can also help to address mismatches between the input and output sequences, since the model can more easily adjust its focus to the relevant positions in the input. Overall, global attention can greatly improve the performance of neural models in a variety of natural language processing tasks.