Why Transformer Model for Natural Language Processing?

A new NLP model understands texts, performs sentiment analysis, etc

A transformer is a new type of neural network architecture. What's grabbing eyeballs is that it has brought in improvements in efficiency and accuracy to tasks like Natural Language Processing. We are well acquainted with other neural architectures like convolutional neural networks and recurrent neural networks. But what makes this transformer architecture stand out from the rest is how it caters to machine learning. Transformer stands the potential to find pattern within the existing data. Far aren't the days when we'd be able to find the application of transformermodels in various commercial applications.

Research work on how to deploy transformer architecture to address issues like detecting anomalies, time series analysis, etc is ongoing on natural language processing.

Researchers have come up with Natural Language Processing models like Google BERT and OpenAI GPT 3.5 capable of performing tasks like understanding text, performing sentiment analysis, answering questions, summarizing reports and generating new text. With these transformer models in place, the researchers can now refine these models for other applications.

How is a transformer different from CNN and RNN?

The best part about deploying transformers is that it combines the benefits of convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Both of these have had wide usage in pattern recognition, recognizing objects in pictures. It was very much possible to process different pixels in parallel to tease apart lines, shapes and whole objects as far as CNN is concerned. Problems arose when the input was text.

RNN served to be a good option for evaluating ongoing streams of things that include strings of text. But, things weren't that smooth when the subject of interest was to model the relationship between words in case of either a long sentence or a para.

This is why the need for a natural language processing model that'd address the above limitations rose. It was then that the researchers at Google discovered that they could achieve way better results by bringing in transformer architecture. Here, the mechanism is such that it uses data, encodes it followed by capturing how any given word relates to other words that come before and after it.

Advantages of transformers

The below advantages of transformers over other natural language processing models are sufficient reasons to rely on them without thinking much-

1. They hold the potential to understand the relationshipbetween sequential elements that are far from each other.
2. They are way more accurate.
3. They pay equal attention to all the elements in the sequence.
4. Transformers boast of faster processing. In other words, they can process more data in lesser time.
5. They could work with virtually any kind of sequential data.
6. They can predict what could happen next.
7. Transformers serve to be helpful in anomaly detection.

The before and after

The advent of transformers enabled the researchers to capture each word into vectors to be able to describe how a particular word relates to other things. It has now become possible to describe words with varied dimensions representing the closeness of the words to the meanings and use of other words.

With transformers in place, the process of modelling the relationship between words became easier than ever.

Application of transformer models –

• Law – In analysing legal records
• Healthcare sector – Analysing medical records and drug interaction
• Virtual assistants
• Marketing, etc.