What Is An Attention Model? Definition, Types And Benefits

By Indeed Editorial Team

Published 5 July 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

The attention mechanism is a significant advancement in machine learning and neural networks. It has led to the development of several discoveries in deep learning like Natural Language Processing (NLP), the Bidirectional Encoder Representations from Transformers (BERT) model and the transformer architecture. Understanding the attention mechanism and how it works can help you advance in your career in NLP, artificial intelligence and machine learning. In this article, we explain the attention model, its types, how it works and discuss its practical applications.

What Is An Attention Model?

An attention model, also known as an attention mechanism, is an input processing technique of neural networks. This mechanism helps neural networks solve complicated tasks by dividing them into smaller areas of attention and processing them sequentially. Just as the human brain solves a complex task by dividing it into simpler tasks and focusing on them one by one, the attention mechanism makes it possible for neural networks to handle intuitive and challenging tasks like translation and generating subtitles. The neural network focuses on specific aspects of a complex input until it categorises the entire dataset.

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Basics Of Attention Mechanism In Neural Networks

As per psychology, attention is the human ability to concentrate on a few specific things while ignoring others. An artificial neural network (ANN) is a series of sophisticated algorithms that aim to mimic the actions of the human brain. Just like the human brain, the attention mechanism attempts to help neural networks selectively concentrate on a few relevant tasks while ignoring others.

To give an example of attention in action, let us say you look at a class group photo. These photos have rows of children sitting or standing along with the class teacher. If someone asks, 'How many children are in the photo?' your brain automatically begins counting the heads of children while ignoring other details in the photo like the colour of the children's uniform. If someone asks, 'Who is the teacher?' your brain looks for individuals with adult features while ignoring the children. This is an example of the attention mechanism automatically done by our brain.

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How The Attention Mechanism Works

Typically, programmers code the attention framework as a function. The function maps the input query and "s set" of value pairs to an output. The input values, keys, query and outcomes are all vectors. The model then calculates the output as a weighted sum of the values. A function that is compatible with the initial query and the corresponding key value is the weight of each value.

The attention framework makes it possible for the neural network to replicate the visual attention mechanism of the human brain. Just like humans do not focus on each word while reading a text, the attention framework allows the neural network to focus on keywords and other important information with intense, high-resolution focus and the other words in low-resolution. The neural network adjusts its focal point as it begins to understand the scene further.

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Drawbacks Of Traditional Neural Networks In Handling Translations

Traditional neural networks struggle to work on long-length translations. The seq2seq model is an example of a traditional neural network used for language modelling. It can handle the transformation of sequential information only when both the input and output are in arbitrary forms. Some of the everyday tasks of the seq2seq model include translating text or audio into other languages.

The seq2seq model uses an encoder and decoder to modify the sequential information into a context vector of fixed length. The problem with this model is that the fixed-length design makes it difficult for the network to remember large sentences. Once the network processes the information, it forgets the beginning parts of the sequence, leading to disjoint translations. The attention mechanism helps to resolve this issue.

Comparing The Working Of Seq2seq And Attention Frameworks

To compare the working of these two models, let us consider the following sentences:

  • Input sentence in English: Shalini is a good girl

  • Output sentence in Hindi: शालिनी एक अच्छी लड़की है

The traditional seq2seq model discards the intermediate states of the encoder and uses only the final vector states to feed the decoder. Here is the illustration of this process:

Shalini -----> Seq 1
is -----> Seq 2
a -----> Seq 3
good -----> Seq 4
girl -----> Seq 5

The seq2seq model discards the encoder outputs from Seq 1 to 5, and the system feeds only the last state of the encoder as input to the decoder. While this technique works for smaller sentences, it becomes a problem as the length of the input sequence increases. It leads to a bottleneck, as it becomes difficult to summarise long sequences into a fixed-length vector. The attention framework does not throw away these intermediate encoder states. Instead, it uses all intermediate states, Seq 1 to 5, to construct the context vectors that the decoder uses to generate the output sequence.

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Benefits Of Using Attention Frameworks

Initially, machine language programmers and neural network scientists used attention frameworks to improve computer vision, the field of study that uses computers to understand digital images and videos. With further developments in this field, programmers use attention mechanisms to improve the performance of neural machine translations (NMT). Traditionally, NMT systems relied on massive libraries of data with complex functions for mapping the statistical properties of each word.

With attention mechanisms, NMT is far simpler and more efficient. The attention framework maps the meaning of a sentence with a fixed-length vector which generates the translation for the entire vector. Unlike conventional encoder-decoder models that translate word for word, attention mechanisms pay attention to the sentence's overall 'high-level' sentiment. This helps to increase the accuracy of each translation and is much faster to train neural networks.

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Types Of Attention Models

Attention mechanisms are of various types based on their sources and the different maps they create. Here are some of the common types of attention mechanisms:

Global attention model

Also known as the soft attention framework, this model collects inputs from all encoder and decoder states before evaluating the current state to generate the output. It uses each encoder step and decoder preview step to align or calculate attention weights. It multiplies the results from each encoder step with globally aligned weights to find the content value to feed to the recurrent neural network (RNN). This model uses values from the RNN to find the decoder's outputs.

Local attention model

Also known as the hard attention model, the local attention mechanism has a similar working structure to the global model. The significant difference is that the local attention mechanism uses only a few encoder positions to calculate the align weights. This model determines the context vector and aligns weights by selecting words from the encoder's source and the first-aligned position. It allows for predictive and monotonic alignment. The predictive alignment enables the model to predict the final alignment position and the monotonic alignment considers only select information. It also combines specific aspects of hard and soft attention.

Self-attention model

The self-attention mechanism focuses on various positions from a single input sequence. You can combine the global and local attention frameworks to create this model. The difference is that it considers the same input sequence instead of focusing on the target output sequence.

When To Use The Attention Framework

Initially, machine-language programmers used attention frameworks to improve the functioning of an encoder-decoder-based neural machine translation system and to enhance computer vision. It used natural language processing (NLP) capabilities and relied on substantial data libraries with complex functions to handle translations. Using attention mechanisms helps assign maps to fixed-length vectors to generate efficient translations. While the output may not be 100% linguistically accurate, the result matches the intention and general sentiment of the initial input.

Attention frameworks are an excellent choice to overcome the potential limits of the encoder-decoder translation model. It helps in accurately aligning and translating input phrases and sentences. Since it develops a context vector for each output, rather than encoding the entire input sequence as a single-fixed content vector, it is helpful for efficient translations.

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Tips For Using Attention Frameworks

If you are implementing an attention framework for work, you can use these tips to execute the model more effectively:

  • Understand the different models. As mentioned above, there are three different frameworks for executing the attention mechanism. Evaluating the features and advantages of each model can help you choose the proper framework for the most accurate results.

  • Provide consistent training to the neural network. Using consistent back-propagation training and reinforcement can help make the attention framework more effective and accurate. This helps identify potential errors in the model and determine how to refine and improve them.

  • Use attention mechanisms for translation projects. Attention mechanisms are best-suited for language translations. Using them frequently helps in improving the accuracy of translations by assigning appropriate weights to different words.


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