---
# System prepended metadata

title: Attention
tags: [Machine learning]

---

# Attention

###### tags: `Machine learning`

## What is Attention?

Attention in machine learning is a mechanism that allows a model to focus on specific parts of the input when processing it, rather than using the entire input equally. This can be especially useful when dealing with long sequences of data, where the model may not need to consider every element in the sequence to make a prediction or decision, and focusing on the most relevant parts of the input data can improve the accuracy and efficiency of the model.

Attention mechanisms can be very useful for a wide range of machine learning tasks, including natural language processing, image classification, and machine translation. In general, attention is naturally applied to Sequnce-to-sequence (Seq2Seq) models 

## Sequence-to-sequence  (Seq2Seq) models 

**Seq2Seq models** are a type of deep learning neural network that is commonly used for tasks such as machine translation, summarization, and text generation. These models are trained to take a sequence of input data, process it, and produce a corresponding sequence of output data.

The key components of a Seq2Seq model are the encoder and the decoder. The encoder processes the input sequence and converts it into a fixed-length representation, known as the **context vector**. This context vector is then passed to the decoder, which uses it to generate the corresponding output sequence.

![](https://i.imgur.com/yfTVq8P.png)

One **advantage** of Seq2Seq models is that they can handle variable-length input and output sequences, making them well-suited for tasks that involve sequential data. They can also handle input sequences with different lengths and produce output sequences of varying lengths, which is useful for tasks such as machine translation where the length of the output sequence may differ from the length of the input sequence.

One **limitation** of Seq2Seq models is that they can struggle to handle long input and output sequences due to the fixed-length representation used by the encoder. To address this issue, variations of the Seq2Seq model have been developed, such as the transformer model, which uses self-attention mechanisms to process input sequences of arbitrary length.

## Bahdanau Attention and Luong Attention

Bahdanau Attention and Luong Attention are two types of attention mechanisms that can be used in natural language processing tasks, particularly in the context of Seq2Seq models such as machine translation and text summarization.

### Bahdanau Attention

Bahdanau Attention, also known as additive attention, was introduced in a 2014 paper by Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. It works by calculating the attention weights for each input time step by comparing the current decoder state with the encoder states. Specifically, it uses a feedforward neural network to compute the attention weights, which are then used to weight the encoder states when generating the next output time step. The weights are thus computed using a weighted sum

$$ c_{i} = \sum_{j=1}^{T_{x}}\alpha_{ij}h_{j} \;\;\;\;\; [Context \;vector] $$

The key difference between Bahdanau Attention and other attention mechanisms is that it uses an **additive attention function**, which combines the hidden state of the decoder and the encoder's output using element-wise addition. This allows the model to learn complex relationships between the input and output sequences.

### Luong Attention

Luong Attention, also known as multiplicative attention, was introduced in a 2015 paper by Minh-Thang Luong, Hieu Pham, and Christopher D. Manning. It works by calculating the attention weights in a similar way to Bahdanau Attention, but uses a **dot product** between the decoder state and the encoder states to compute the attention weights. This allows the attention weights to be computed more efficiently, as the dot product is a simple and fast operation.

![](https://i.imgur.com/Nej4Exa.png)

Both Bahdanau Attention and Luong Attention have been widely used in natural language processing tasks, and have shown to be effective in improving the performance of sequence-to-sequence models. In general, attention mechanisms allow the model to focus on specific parts of the input sequence when generating the output, which can help the model to better capture the relationships between the input and output sequences.

## Attention masks

Another way that attention can be used in machine learning is through the use of **attention masks**. These are fixed patterns of weights that are applied to the input data to give certain parts more or less attention. 
There are several ways in which attention masks can be used to improve the performance of **NLP models**. For example, they can be used to focus the model's attention on specific words or phrases that are important for understanding the context of the input text. Also, a mask might be used to tell the model to pay more attention to the beginning of a sentence, or to the last few words in a paragraph. They can also be used to prevent the model from getting stuck in a loop, which can sometimes occur when the model is processing long sequences of text.

## To sum up

In the encoding stage of natural language processing tasks, attention is a technique used to compile relevant information about the input by considering both the current and past states of the encoder. This compiled representation, known as a context vector, is passed to the decoder and is used to generate the desired output. The context vector is created through a trainable, weighted sum of scores calculated using a score function, such as those proposed by Bahnadau and Luong. Attention allows the decoder to focus on specific parts of the input, enabling it to better understand the context and generate a more accurate output.