The scope of innovation in Artificial Intelligence (AI) is vast and the field of Natural Language Processing (NLP) is setting new benchmarks for it every day. What is redefining the way we approach the completion of tasks through NLP is a novel architecture known as Transformer-based architecture. The dynamic problem solving employed using NLP is enhanced tenfold through this novel architectural model.
But first, let us understand what NLP is. NLP can be defined in simple terms by saying that it is the human-like manipulation of speech or text using the software. The study of NLP began with the study of linguistics, including grammar and semantics, and progressed through computational linguistics. More recently, it has taken a more engineer-based or empirical approach through statistical methods.
In this article, we will discuss how machine learning practitioners can leverage transformer models and architectures to implement NLP. We will also jot down a few reasons why transformer models are favored over other NLP models.
What Are Transformers In NLP?
Transformer models are architectures that are trained with a semi-supervised approach using millions of lines of text. These models work with a novel architecture that relies almost entirely on self-attention to compute representations of its input and output. Sequence-to-sequence tasks are solved using sequence-aligned Recurrent Neural Networks (RNN).
RNN is used frequently in NLP because of its capability in building efficient and accurate language models. Combined with transformers, RNN provides some of the fastest answers for implementing NLP solutions for text and speech processing. Transformer models achieve more state-of-the-art results that are far more efficient than other models utilizing RNN.
The context of a word in a sentence that arises from its connection with another phrase in the sentence can be computed in a more efficient way with transformer models. For instance, let us take the following phrase:
"making the feasibility of the event more difficult"
Here, "more difficult" is distant from the word "making", although the former is a dependency that is obvious to the human mind observing this phrase. Transformer models can make similar inferences about the inter-dependencies of various phrases or words in a sentence. This is as close as AI can come to human intelligence in processing language. This youtube video can give you a better understanding of why understanding phrases and dependencies are crucial for understanding context.
Why Are Transformer Models Optimal For NLP?
The scope of finding patterns within speech and text-related datasets using NLP increases exponentially with the implementation of transformer models. Additionally, issues such as anomaly detection and time-series analytics can also be addressed if the right transformer architectures are deployed for each particular problem.
The advantages brought by different types of neural networks such as convolutional (CNN) and RNN get combined together with the deployment of transformer models. However, the limitations that these neural networks face when working with text are completely eliminated. Transformer models use a particular method, wherein they capture data, say a word, encode it and then capture how this word relates to other words coming before and after it.
These models digest large volumes of textual data, becoming very powerful language models that are capable of accurately understanding context. Predictive analytics based on inferences from textual analysis then becomes an easy next step in the process. They enable AI to not only conduct simple text comprehension but also make higher-level connections based on emotions and feelings.
Main Benefits Of Transformer Models
Transformer-based NLP opens up possibilities for limitless and exhaustive training of textual analysis models. This then provides AI-based textual analyzers with the capability to read through several lines of text with no loss of efficiency. The following are some other detailed benefits that arise out of implementing transformer models:
1)Handling Sequential Data:
Sequential data could consist of data that maintains a set order such as in terms of time series data or a grammatical sentence or phrase. Transformers utilize an encoder-decoder framework which is perfectly suited to handle several paragraphs of textual input every second. The textual data is mapped into a representational space by the encoder which is then mapped to the output via the decoder. The transformer architecture converts sequences of numbers, text, and audio or video into versatile NLP tasks.
2)Pre-Training Data Sets:
Transformer models can be pre-trained with millions of data sets of rich text and other forms of data to speed up the implementation of NLP. As transformers have evolved over the years with a deep understanding of language, it does not take a lot of training to develop functional inferences from textual data. With the preliminary analysis of grammar and context already out of the way, the trainer can focus on specific goal achievement using NLP. These specializations may include named-entity recognition, language generation from given data, as well as conceptual understanding of language. Transformer models are highly versatile and have the capability to scale across business verticals with sufficient ease.
3)Out-Of-The-Box Implementation
Building solutions for text-based analysis involves sorting through documents, labeling keywords, and identifying individual sentences' sentiments, which can be highly cumbersome tasks. When the text analysis is highly time-critical, you can leverage out-of-the-box solutions which are pre-trained NLP models that automate certain parts of the process. When the data scientist needs to understand higher-level items like categories or concepts within the documents, out-of-the-box solutions can again come in handy. Additionally NLP models trained using these methods tend to be suited to multiple industries and can be implemented using general purpose data.
4)Better Sentiment Analysis
Say you need to train a large-scale language model on the complete works of a renowned author. This would involve a massive amount of text parsing and then finding the essential parts of the collected text documents would require very intricate sentiment analysis. Transformer models allow for all these capabilities and also the added benefit of making analytical predictions for future use of the trained language models. As the pre-trained model already understands the sentiments within the language, it can make NLP much more customizable and scalable when used for other data sets.
ALSO READ: What are Language Models in NLP?
Expanding The Scope Of NLP Through Transformer Models
Most of the leading NLP implementation models have been majorly replaced by transformer models. If you can ascertain exactly what NLP task you need to conduct, then the next step would be to simply determine which transformer-based approach would be best suited for this purpose.
After choosing the right approach for your textual or speech dataset, all you need to do is fine-tune it or find a model that is already fine-tuned. This and several other benefits make transformer models perfectly suited for implementing NLP. You can learn how to leverage several capabilities of AI to model your business data as per your market strategies by setting up a free consultation with Daffodil.