Intuition Behind Self-Attention Mechanism in Transformer Networks

Name: Intuition Behind Self-Attention Mechanism in Transformer Networks
Uploaded: 2020-10-17T04:58:18.000Z
Duration: 1 h 18 min 24 s

Introduction to Self-Attention Mechanism in Transformer Networks

In this video, the speaker introduces the concept of self-attention mechanism in transformer networks and its importance in natural language processing.

Attention Mechanism and Transformer Networks

Attention mechanism was introduced in 2014, but transformer networks using attention mechanism were introduced in 2017.

Transformer networks such as BERT, GPT2, and GPT3 have been successful with these architectures.

The focus of this video is on multi-head attention and vanilla transformer network as described in the "Attention is All You Need" paper.

Importance of Contextual Information

Understanding the context of a sentence is important to determine the meaning of a word.

The meaning of a word can change depending on its surrounding words.

Contextual information can be obtained from neighboring words through a self-attention mechanism.

Tokenization and Vectorization

Tokenization involves breaking down a sentence into individual words or tokens.

Neural networks do not understand English words, so tokenized words must be vectorized into numerical values.

Word vectors have semantic meaning associated with them and are used to cluster similar meanings together.

Self-Attention Mechanism

Purpose of Self-Attention Mechanism

The main purpose of self-detention mechanism is to get contextual information for words in a sentence.

Example

An example sentence "I swam across the river to get to the other bank" is used to demonstrate how contextual information can be used to determine the meaning of a word.

Tokenization and vectorization are used to convert words into numerical values.

Word vectors have semantic meaning associated with them and are used to cluster similar meanings together.

Self-Attention Mechanisms

In this section, the speaker explains self-attention mechanisms and how they work.

Dot Product Scoring

The dot product is used to get scores between word vectors.

If two vectors are similar, their dot product will be big.

If two vectors are perpendicular, their dot product will be zero.

If two vectors are opposite in direction, their dot product will be negative.

Self-Attention Mechanism

The self-attention mechanism takes word vectors as input and weighs them according to context.

After adding context from a sentence, the contextualized representation of a vector may change.

Depending on the context, the word "bank" could be closer in meaning to water bodies like rivers and canals than financial institutions.

Dot Product Calculation

Dot products are calculated for every possible pair of word vectors.

The resulting scores indicate how close the meanings of words are to each other.

Graphical Representation

A graphical representation shows that a word is closest to itself.

Introduction to Cell Attention Mechanism

In this section, the speaker introduces the concept of cell attention mechanism and explains how it works.

How Cell Attention Mechanism Works

The speaker explains that the word "swam" has a higher score when compared to the words "river" and "bank".

Scores are obtained by dot product between vectors.

Each element of a vector can be seen as a box in a row. If the score is high, the box is white.

Scores are normalized using softmax to obtain weights.

Weights are used to weigh original word vectors.

Weighted vectors become contextualized representations of original word vectors.

Importance of Good Word Vectors

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Normalizing with Softmax

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Weighing Original Word Vectors with Weights

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Conclusion

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Contextualized Word Embeddings

In this section, the speaker explains how to get scores by dot product normalization with the soft mix function way original word vectors. The speaker provides a concrete example of three words represented by three vectors and how to find the contextualized representation of a word vector.

Finding Contextualized Representation of a Word Vector

To enrich the word vector v2, we want to find its contextualized representation.

We do a dot product between b1 and b2, b2 and v2, and b2 and b3 to get s21, s22, and s23 respectively.

Normalize these scores to get w21, w22, and w23.

Multiply these weights with our vectors (v1,v2,v3) element-wise to weigh them.

The resulting vector is y2 which is a contextualized representation of our original word vector v2.

Attention Mechanism

An attention mechanism or self-attention mechanism is introduced where keys are matched up with queries using dot products.

Query weights key weights and value weights are introduced as linear layers with tunable weights for optimization during backpropagation.

Conclusion

The module is simple after introducing query weights key weights and value weights.

Dot Product Attention and Scaled Dot Product Attention

In this section, the speaker explains how to use dot product attention and scaled dot product attention. The speaker also provides an intuition behind scaling.

Dot Product Attention

Use matrix multiplication with weight values to get output.

Resulting vector is the same dimension as input vector.

Do matrix multiplication with query weights (q) and key weights (k), skip scale and mask, then do a softmax function.

Scale is an element-wise multiplication with 1 over square root of dk where dk refers to the dimension of input word vectors.

Scaled Dot Product Attention

Scaling helps when your vector has high dimensions.

Scaling makes sure that score is a well-behaving number for softmax function.

Softmax function will have a good gradient when finding the gradient of it.

Equation in paper: matrix multiplication between q and k, scale it with square root of dk, take a softmax, then do matrix multiplication with v.

Multiple Types of Attention

In this section, the speaker explains that multiple types of attention are used when looking at a word.

When looking at a word, multiple types of attention are used to pay attention to multiple things.

For example, if you see the sentence "I swam across the river to get to the other bank" and you're looking at the word "swim", you might be paying attention to different things such as the river, the act of swimming, and the destination.

Attention Mechanism and Multi-Head Attention

In this section, the speaker discusses the importance of attention mechanisms in natural language processing tasks. They explain how a single attention head may not be sufficient for complex tasks and introduce multi-head attention as a solution.

Introduction to Attention Mechanisms

An attention mechanism is used to focus on specific parts of input data.

A single attention head may not be enough for complex tasks.

Using a single attention head can lead to overfitting and poor performance.

Multi-Head Attention

Multi-head attention allows for multiple heads to attend to different aspects of the input data.

Each head has its own set of weights for queries, keys, and values.

The output from each head is concatenated and passed through another linear layer to produce the final output.

Multi-head attention produces contextual representations that are specialized for specific tasks.

Applications of Transformers with Multi-Head Attention

Transformers with multi-head attention are commonly used in natural language processing tasks.

They are also being used in vision-related tasks such as object detection.

Transformers for Vision Tasks

In this section, the speaker discusses how transformers can be used to replace convolutional neural networks in vision tasks.

Replacing Convolutional Neural Networks with Transformers

A paper called "An Image is Worth 16 by 60 Words" uses transformers to completely replace convolutional neural networks in vision tasks.

Multi-head attention is used for vision tasks, with queries, keys, and values being used in the same way as before.

The purpose of understanding the components of self-attention mechanisms is to identify weaknesses and improve upon them. Researchers who want to advance this architecture must understand it from first principles.

Importance of Understanding First Principles

Understanding things from first principles is important for researchers who want to advance deep learning architectures.

It helps researchers identify weaknesses and build better models.

Even if you're not doing new research, understanding first principles can help you read papers with greater intuition.

Conclusion

The purpose of this video is to provide an intuition for why self-attention mechanisms are built the way they are.

By understanding these mechanisms from first principles, viewers can revisit transformer architectures with a better understanding.