Study with ChatPDF: attention mask, masked word activities, multi-word feature creation matrix

15 min readMay 28, 2023

Me: “The query Q represents the feature of interest and the matrix K represents the collection of masks. “ can you explain it with an example?

PDF: Certainly! Let’s say we have a sentence “The cat sat on the mat.” and we want to use a Transformer model to predict the next word in the sequence. We can represent each word in the sentence as a vector of numbers using an embedding layer.

Now, let’s say we want to pay extra attention to certain words in the sentence when making our prediction. We can create a mask for this purpose, where each element of the mask corresponds to one of the words in the sentence. For example, we might create a mask that gives high values to “cat” and “mat”, indicating that these words are particularly important for predicting the next word.

We can represent this mask as a matrix K, where each row corresponds to one of the words in the sentence and each column corresponds to one of the elements of the mask. The query Q would be another vector representing some feature of interest, such as the current state of our model.

To compute attention scores between Q and each word in the sentence, we would multiply Q by K^T (the transpose of K). This would give us a vector of scores indicating how much attention we should pay to each word when making our prediction. We could then use these scores to weight our embeddings before passing them through subsequent layers of our Transformer model.

Me: how the matrix K is built by machine?

PDF: In the context of natural language processing, the mask matrix K is typically built using a one-hot encoding scheme.

To create the mask for a given sentence or sequence of words, we first define a set of “key” words or phrases that we want our model to pay attention to. We then create a binary vector for each word in the sequence, where the vector has a value of 1 in the position corresponding to the key word(s) and 0 elsewhere.

These binary vectors are then stacked together into a matrix K, where each row corresponds to one of the words in the sequence and each column corresponds to one of the key words or phrases. This matrix can then be used as part of the attention mechanism in a Transformer model.

It’s worth noting that there are many different ways to define and construct attention masks, and researchers are constantly exploring new techniques for doing so. However, one-hot encoding is a common and relatively simple approach that can be effective in many cases.

Me: one-hote encoding is not being used due to its computation demand, using embedding instead, right?

PDF: Yes, that’s correct! One-hot encoding can be computationally expensive, especially for large vocabularies. Instead, most modern natural language processing models use embedding layers to represent words as dense vectors of real numbers.

These embeddings are learned during training and are optimized to capture semantic relationships between words. By using embeddings instead of one-hot encoding, we can reduce the dimensionality of our input data and make it easier to compute attention scores and other operations in our model.

That being said, one-hot encoding is still used in some contexts where the vocabulary size is small or where sparsity is desirable (e.g., in some types of recommendation systems). But for most natural language processing tasks, embeddings have become the standard way of representing words as input to a machine learning model.

Me: how the embedding is built?

PDF: Embeddings are typically learned during the training process of a neural network model. The embedding layer is initialized with random values, and then the weights of the embedding layer are updated during training using backpropagation.

The goal of the embedding layer is to learn a set of dense vectors that represent each word in our vocabulary. These vectors should capture semantic relationships between words, such that words with similar meanings or contexts have similar embeddings.

During training, the model receives input data in the form of sequences of words (e.g., sentences or paragraphs). Each word in the sequence is represented as an integer index corresponding to its position in the vocabulary. These integer indices are then passed through the embedding layer, which maps each index to a dense vector representation.

The specific details of how embeddings are learned can vary depending on the architecture and objective function of the model. For example, some models may use pre-trained embeddings that have been learned on large corpora of text data, while others may learn embeddings from scratch as part of a larger neural network architecture.

Overall, however, the goal is always to learn a set of dense vector representations that capture meaningful semantic relationships between words and can be used as input to downstream machine learning tasks.

Me: how to learn the dense vector representations?

PDF: The process of learning dense vector representations for words is typically done using an unsupervised learning technique called word embedding. There are several methods for learning word embeddings, but one of the most popular is the Word2Vec algorithm.

The Word2Vec algorithm is a neural network-based approach that learns word embeddings by predicting the context in which words appear in a large corpus of text data. The basic idea is to train a neural network to predict the probability of observing a given word in a particular context (i.e., the surrounding words).

During training, the model receives input data in the form of sequences of words (e.g., sentences or paragraphs). Each word in the sequence is represented as an integer index corresponding to its position in the vocabulary. These integer indices are then passed through an embedding layer, which maps each index to a dense vector representation.

The output of the embedding layer is then fed into a neural network that predicts the probability of observing each word in its context. The weights of both the embedding layer and the neural network are updated during training using backpropagation.

By optimizing this objective function over many iterations, Word2Vec learns dense vector representations for each word that capture meaningful semantic relationships between words. Words that appear in similar contexts will have similar embeddings, while words that appear in different contexts will have dissimilar embeddings.

Overall, this process allows us to learn high-quality dense vector representations for words that can be used as input to downstream machine learning tasks such as sentiment analysis or machine translation.

Me: is the word2vec using deep learning?

PDF: Yes, Word2Vec is a deep learning algorithm that uses a neural network architecture to learn word embeddings. Specifically, it uses a shallow neural network with one hidden layer to predict the context in which words appear in a large corpus of text data.

The input layer of the network takes in a one-hot encoded vector representing the target word, and the output layer produces a probability distribution over all the words in the vocabulary. The hidden layer of the network represents the learned embedding for each word.

During training, the weights of both the input and output layers are updated using backpropagation based on how well the model predicts each word’s context. This process allows Word2Vec to learn high-quality dense vector representations for words that capture meaningful semantic relationships between them.

Overall, Word2Vec is an example of how deep learning techniques can be used to learn powerful representations of natural language data that can be used as input to downstream machine learning tasks.

Me: how the attention mask with embedding?

PDF: The attention mask is used in conjunction with the word embeddings to compute the attention scores between different parts of a sequence. The attention mask is a binary matrix that indicates which parts of the sequence should be attended to and which parts should be ignored.

In the context of natural language processing, the attention mask is typically used to handle variable-length input sequences. For example, in a language translation task, the input sentence may have a different length than the output sentence. To handle this, we can use an attention mask to indicate which parts of the input sequence correspond to each part of the output sequence.

To compute the attention scores between different parts of a sequence, we first apply linear transformations to both the query and key embeddings. These transformed embeddings are then multiplied together element-wise and summed along their last dimension (i.e., their inner product). The resulting scores are then scaled by a factor of 1/sqrt(d_k), where d_k is the dimensionality of the key embeddings.

Finally, we apply an element-wise softmax function to these scores to obtain a probability distribution over all positions in the input sequence. The attention mask is then applied by setting all masked positions in this probability distribution to zero before normalizing it again.

Overall, this process allows us to compute attention scores between different parts of a variable-length input sequence while ignoring irrelevant or padded positions using an attention mask.

Me: what the attention mask looks like?

PDF: The attention mask is a binary matrix that has the same shape as the attention scores between different parts of a sequence. It has a value of 1 for positions in the input sequence that should be attended to and a value of 0 for positions that should be ignored.

The specific structure and values of the attention mask depend on the task and model architecture being used. In some cases, it may be a diagonal matrix with ones along its main diagonal and zeros elsewhere, indicating that each position in the output sequence should only attend to its corresponding position in the input sequence. In other cases, it may be more complex, with different patterns of ones and zeros depending on the specific requirements of the task.

Overall, the attention mask is an important component of many modern deep learning models for natural language processing that allows them to handle variable-length input sequences while ignoring irrelevant or padded positions.

Me: these attention mask is inited from random number?

PDF: No, the attention mask is not initialized from random numbers. Instead, it is typically constructed based on the specific requirements of the task and the model architecture being used.

In some cases, the attention mask may be a fixed matrix that is determined before training begins. For example, in a language translation task where the input and output sequences have different lengths, the attention mask may be a diagonal matrix with ones along its main diagonal and zeros elsewhere, indicating that each position in the output sequence should only attend to its corresponding position in the input sequence.

In other cases, such as when using self-attention mechanisms in transformer models, the attention mask may be dynamically generated during each forward pass of the model based on which positions in the input sequence are padded or masked. This allows the model to handle variable-length input sequences without having to precompute a fixed attention mask.

Overall, while there may be some randomness involved in how attention masks are generated or applied during training (e.g., due to stochastic gradient descent), they are not initialized from random numbers like other parameters in a neural network.

Me: can you explain more for how the attention mask is made in self-attention, transformer?

PDF: In self-attention mechanisms used in transformer models, the attention mask is dynamically generated during each forward pass of the model based on which positions in the input sequence are padded or masked. This allows the model to handle variable-length input sequences without having to precompute a fixed attention mask.

The attention mask used in self-attention mechanisms is typically a binary matrix with ones indicating valid positions and zeros indicating masked positions. There are two types of masks that can be used in self-attention: padding masks and future masks.

Padding masks are used to ignore padded positions in the input sequence. In natural language processing tasks, it is common to pad shorter sentences with zeros so that all sentences have the same length. The padding mask is a binary matrix with ones indicating valid positions and zeros indicating padded positions.

Future masks are used to prevent information from future tokens from leaking into the current token during training. In other words, they ensure that each token can only attend to previous tokens in the sequence. The future mask is an upper triangular binary matrix with ones indicating valid positions and zeros indicating invalid (future) positions.

To generate these masks, we first create a tensor of shape (batch_size, num_heads, seq_len_q, seq_len_k) where batch_size is the number of examples in a batch, num_heads is the number of attention heads, seq_len_q is the length of the query sequence, and seq_len_k is the length of the key sequence.

For padding masks, we set all elements corresponding to padded positions to zero and all other elements to one. For future masks, we set all elements corresponding to invalid (future) positions to zero and all other elements to one.

Finally, we apply these masks by multiplying them element-wise with the attention scores between different parts of a sequence before computing softmax probabilities over them. This ensures that only valid positions contribute to computing attention scores while ignoring irrelevant or padded positions using an attention mask.

Me: is the attention mask same as masked word activities here?

PDF: The attention mask and masked word activities are related concepts, but they are not exactly the same thing.

The attention mask is a binary matrix that is used to control which parts of a sequence should be attended to during self-attention in transformer models. It is typically used to handle variable-length input sequences and can be dynamically generated during each forward pass of the model based on which positions in the input sequence are padded or masked.

Masked word activities, on the other hand, refer to a specific type of pre-training task used in transformer models called masked language modeling (MLM). In MLM, some percentage of the input tokens are randomly replaced with a special [MASK] token, and the model is trained to predict the original token given its context. This forces the model to learn contextual representations of words that can be used for downstream tasks like language translation or sentiment analysis.

During training for MLM, an attention mask is also used to ensure that the model only attends to valid tokens when predicting masked words. Specifically, all [MASK] tokens are replaced with either a random token or their original token with probability 0.5 before being fed into the model. The attention mask is then applied during self-attention to ensure that each token can only attend to previous tokens in the sequence and not future tokens or [MASK] tokens.

So while both attention masks and masked word activities are important components of transformer models for natural language processing, they serve slightly different purposes and are used in different contexts.

Me: what is multi-word feature creation matrix in the paper?

PDF: In the paper referenced on page 17, the multi-word feature creation matrix refers to a method for creating features that capture the co-occurrence of multiple words in a sequence. Specifically, the authors propose using a matrix multiplication-based approach to create features that consist of pairs of words that occur together in a sequence.

The multi-word feature creation matrix is learned during training and can be used to build other types of features beyond just pairs of words. For example, it could be used to create three-word combinations or other structures based on the co-occurrence patterns observed in the training data.

The basic idea behind this approach is to represent each word in a sequence as a one-hot vector and then multiply it by the learned feature creation matrix to obtain a new vector that represents all possible pairs of words that occur together with that word. This process is repeated for each word in the sequence, resulting in a matrix where each row represents all possible pairs of words that occur with a given word.

These matrices can then be used as input to downstream models for tasks like sentiment analysis or language modeling. By capturing co-occurrence patterns between words, they can help improve model performance by providing additional context and information about how different words relate to each other within a sentence or document.

Me: in this paper, it seems to me is that they use masked word activities to multiply multi-word feature creation matrix, not one-hot vector.

PDF: You are correct that in the paper referenced on page 17, the authors use masked word activities to multiply the multi-word feature creation matrix, rather than one-hot vectors.

Specifically, they use a variant of the masked language modeling (MLM) pre-training task, where some percentage of the input tokens are randomly replaced with a special [MASK] token and the model is trained to predict the original token given its context. During this pre-training task, they use a learned multi-word feature creation matrix to capture co-occurrence patterns between words and generate features that consist of pairs of words that occur together in a sequence.

To apply this matrix during MLM pre-training, they first replace some percentage of input tokens with [MASK] tokens and then multiply each input token by the learned feature creation matrix to obtain a new vector that represents all possible pairs of words that occur together with that word. This process is repeated for each token in the sequence, resulting in a matrix where each row represents all possible pairs of words that occur with a given word.

This masked word activity matrix is then used as input to downstream models for tasks like sentiment analysis or language modeling. By capturing co-occurrence patterns between words using this method, they can help improve model performance by providing additional context and information about how different words relate to each other within a sentence or document.

Me: in this paper, where the masked word activities is coming from

PDF: In the paper referenced on page 17, the masked word activities are generated as part of a pre-training task called masked language modeling (MLM).

During MLM, some percentage of the input tokens are randomly replaced with a special [MASK] token, and the model is trained to predict the original token given its context. This forces the model to learn contextual representations of words that can be used for downstream tasks like language translation or sentiment analysis.

To apply MLM during pre-training, the authors replace some percentage of input tokens with [MASK] tokens and then use a learned multi-word feature creation matrix to capture co-occurrence patterns between words and generate features that consist of pairs of words that occur together in a sequence. This process results in a masked word activity matrix that is used as input to downstream models.

The masked word activity matrix is essentially a binary matrix that indicates which tokens in the input sequence were replaced with [MASK] tokens during MLM pre-training. It is used during self-attention to ensure that each token can only attend to previous tokens in the sequence and not future tokens or [MASK] tokens. By masking out these tokens during self-attention, the model can learn to better handle variable-length input sequences and improve its ability to generate accurate predictions for downstream tasks.

Me: how it relates to attention score?

PDF: The masked word activity matrix generated during MLM pre-training is used in conjunction with the attention mechanism to calculate attention scores for each token in the input sequence.

During self-attention, each token in the input sequence attends to all other tokens in the sequence, including itself. The attention scores are calculated by taking the dot product of a query vector (which is derived from the current token) and a key vector (which is derived from each of the other tokens), and then applying a softmax function to obtain a probability distribution over all tokens.

However, when calculating attention scores, it is important to mask out any tokens that were replaced with [MASK] tokens during MLM pre-training. This is because these tokens do not provide any useful information for downstream tasks and can lead to inaccurate predictions if they are included in the attention calculation.

To accomplish this, the masked word activity matrix is used to mask out any [MASK] tokens during self-attention. Specifically, any token that was replaced with a [MASK] token during MLM pre-training will have a corresponding entry of 0 in the masked word activity matrix. When calculating attention scores, these entries are set to -infinity before applying softmax, effectively removing them from consideration and ensuring that each token only attends to previous non-masked tokens in the sequence.

By using both the attention mechanism and masked word activity matrix together in this way, models can learn to better handle variable-length input sequences and generate more accurate predictions for downstream tasks.

Me: so, can you give me an example how is the masked word activities built?

PDF: Sure, I can give you an example of how the masked word activities are built during MLM pre-training.

Let’s say we have the following input sequence:

“the quick brown fox jumps over the lazy dog”

To generate masked word activities, we first randomly replace some percentage of the input tokens with a special [MASK] token. Let’s say we replace 15% of the tokens in this example, resulting in the following masked sequence:

“the quick [MASK] fox jumps over [MASK] lazy dog”

Next, we use a learned multi-word feature creation matrix to capture co-occurrence patterns between words and generate features that consist of pairs of words that occur together in a sequence. This matrix is typically learned during pre-training and can be thought of as a lookup table that maps each word to a vector representation.

For example, let’s say our feature creation matrix has learned that “quick brown” and “lazy dog” often occur together in sequences. We would represent these pairs as vectors and use them to generate features for each token in the input sequence.

To do this, we first convert each token in the input sequence to its corresponding vector representation using the feature creation matrix. For example, “the” might be represented by a vector [0.2, 0.3], “quick” might be represented by [0.5, 0.1], and so on.

We then multiply each token vector by the feature creation matrix to obtain a new vector that represents all possible pairs of words that occur together with that word. For example, if we multiply the vector for “quick” by our feature creation matrix, we might obtain a new vector that represents both “quick brown” and “quick fox”.

This process is repeated for each token in the sequence (including any [MASK] tokens), resulting in a matrix where each row represents all possible pairs of words that occur with a given word. This matrix is the masked word activity matrix, and it is used during self-attention to ensure that each token can only attend to previous tokens in the sequence and not future tokens or [MASK] tokens.