Sep 06

Pros and cons of different NLP architectures

Natural Language Processing (NLP) is an aspect of artificial intelligence that enables computers to understand, interpret, and use human language. NLP allows computers to communicate with humans using human language. NLP also allows computers to read, hear, and interpret text. NLP aims to bridge the gap between human-computer communication by leveraging various disciplines such as computational linguistics and computer science.
In general, NLP divides the language into short, basic parts called tokens (words, periods, etc.) and tries to understand the relationships between the tokens.
In the ever-evolving field of NLP, the choice of architecture is pivotal. Each NLP architecture comes with its own set of advantages and drawbacks, influencing how they perform in various applications.

In this blog post, we will dissect the pros and cons of different NLP architectures. From scalability challenges to interpretability and resource requirements, we'll dive deep into the world of NLP architecture to provide you with valuable insights.

Background and history of NLP architectures

Before we delve into the specific aspects of NLP architectures, let's briefly touch upon the historical context. In the early 1900s, the death of a Swiss linguistics professor named Ferdinand de Saussure nearly deprived the world of the notion of "language as science" that would eventually lead to natural language processing. From 1906 to his 1911, Professor Saussure delivered his three courses at the University of Geneva, where he developed an approach to describing language as a 'system'. Within language, sounds represent concepts, concepts whose meaning changes as the context changes.

Saussure argued that meaning arises within language, in the relationships and differences between parts of language. He proposed that 'meaning' arises in the relationship and contrast of languages. A common language system enables communication. Saussure saw society as a system of 'common' social norms that provide the conditions for rational, 'extended' thinking that guides individual choices and actions. (The same idea applies to modern computer languages.)

Saussure died (1913) before he could publish his theory. But two of his colleagues, Albert Sechehaye and Charles Barry, recognized the importance of his conception (a few days after Saussure's death, Sechehay and Barry had coffee together while discussing his discovery). They took the unusual step of collecting "manuscript notes" and "student notes" from the course. On this basis, they wrote the Cours de Linguistique Générale (Course in General Linguistics), published in 1916. This book laid the foundation for the so-called structuralist approach, which began in linguistics and was later extended to other fields, including computers.

In 1950, Alan Turing wrote an article describing the testing of "thinking" machines. If a machine can participate in teletypewriter conversations and imitate humans so perfectly that there are no discernible differences, then the machine can be considered capable of thinking, he said. Shortly thereafter, in 1952, Hodgkin Huxley his model showed how the brain uses neurons to form electrical networks. These events helped inspire ideas for the development of artificial intelligence (AI), NLP, and computers. NLP, at its core, is the science of making machines understand and generate human language. To explore the origins and early developments of NLP, you can refer to this informative article.

Natural Language Processing

Scaling NLP architectures for real-world applications

Scaling NLP architectures to handle large volumes of text data is a critical challenge in today's data-driven world. NLP models must grapple with the immense complexity of human language while maintaining efficiency.

The challenges are multifaceted, including optimizing algorithms and leveraging parallelism. Complex NLP applications require basic linguistic processing engines such as part-of-speech (POS) tagging, named entity recognition and classification (NERC), parsing, and co-reference resolution to handle more complex tasks. These basic NLP modules are used as building blocks to form the complex processing chains required for end-user applications such as information extraction, question answering, and sentiment analysis. Building scalable NLP applications requires designing solutions that allow distributed programs to run in parallel across large clusters of machines. Parallelism can be effectively implemented at several levels.

Perhaps the most effective way to achieve full parallelism is to re-implement all core speech processor algorithms and procedures and adapt them to follow some well-known paradigms such as MapReduce jobs. In this way, NLP can take full advantage of the possibilities offered by large-scale computing frameworks such as Apache Hadoop2.

However, NLP modules are complex software implemented using a variety of programming languages ​​and often require the integration of third-party libraries and dependencies to function properly. Even if it were feasible, it would take an enormous amount of time to adapt each NLP module in the processing chain and re-implement it according to the Map Reduce paradigm.

Moreover, in a rapidly evolving field like NLP, new algorithms and tools are constantly being deployed, and new tools perform the task better and more efficiently, so there is a significant risk of component obsolescence. For a deeper dive into this topic, you can refer to this research paper on scalable architecture for data-intensive NLP.

Read more: Text preprocessing techniques in natural language processing

a woman sitting in front of her laptop and computer screen to work on data analysis and coding

The trade-offs: interpretability vs. explainability

Interpreting the decisions made by NLP models is crucial for trust and accountability. However, complex architectures often create a trade-off between interpretability and explainability. Deciphering why a model made a specific decision can be challenging. In this context, understanding the trade-offs between NLP architectures is vital. To explore this further, check out this insightful article on interpretable and explainable machine learning.

“It’s true there’s been a lot of work on trying to apply statistical models to various linguistic problems. I think there have been some successes, but a lot of failures. There is a notion of success … which I think is novel in the history of science. It interprets success as approximating unanalyzed data.” — Noam Chomsky.

He mentioned, the notion of success is not a success. Well, the lacunae could be the theoretical foundations, but empirically, it could be thought of as the “Interpretability”, which accounts for analysability, transparency, accountability, and explainability of these computational models.

Interpreting NLP models becomes increasingly challenging as the models grow in complexity. Deep learning architectures, including Transformers and neural networks, can have millions or even billions of parameters. Understanding how each parameter influences the model's decision is virtually impossible for a human.

To address these challenges, researchers are actively working on techniques and tools to enhance the explainability of NLP models. This involves creating visualizations, generating human-readable explanations, and identifying critical features in the input data that influence the output.

The trade-off between interpretability and explainability also raises ethical concerns. In applications like healthcare, where NLP models aid in diagnosis, it's crucial to have not only accurate models but also transparent ones. Users must be able to trust the decisions made by these models, which requires a careful balance between complexity and interpretability.

Resource requirements and their implications

Resource requirements, including computational power and data, vary significantly among different NLP architectures. Understanding these resource demands is crucial for deployment, cost management, and accessibility. High resource intensiveness can limit the deployment of NLP solutions in resource-constrained environments.

Deep learning-based NLP architectures often demand substantial computational resources. Training large Transformer models, for example, can require expensive GPU clusters and extensive memory. Besides computational resources, data requirements are a significant consideration. Training NLP models effectively often necessitates vast amounts of labeled data, which may not be readily available for every application. The resource intensiveness of certain NLP architectures can limit their accessibility. Smaller organizations or researchers with limited budgets may struggle to harness the power of these models effectively. This accessibility gap raises questions about democratizing NLP technology. For an in-depth look into the resource implications, refer to this collection of prerequisite resources for NLP, available here.

To evaluate and interpret natural language data, such as text or speech, NLP requires an integration of machine learning techniques. The most common evaluation metrics include accuracy, precision, recall and f1-score.

TP: True Positive Prediction

TN: True Negative Prediction

FP: False-Positive Prediction

FN: False-Negative Prediction

Accuracy = (TP + TN) / (TP + TN + FP + FN)

Precision = (TP) / (TP + FP)

Recall = (TP) / (TP + FN)

F1-Score = 2 * (Precision * Recall) / (Precision + Recall)

Model Selection is the process of selecting an appropriate machine learning model or deep learning model for an NLP application. The dataset will be divided into training and testing sections, based on which the model will be trained and then tested. NLP pre-trained models are useful for NLP tasks like translating text, predicting missing parts of a sentence or even generating new sentences. NLP pre-trained models can be used in many NLP applications such as chatbots and NLP API, etc.

Read more: How to develop a good chatbot

A woman speaking to voice assistant on her smartphone

The landscape of NLP continues to evolve, making it an exciting and dynamic field to explore. NLP architectures play a pivotal role in shaping the capabilities of language processing models.

NLP architectures are not one-size-fits-all, each architecture has its strengths and limitations. The choice of architecture depends on the specific requirements of your NLP application. By understanding the pros and cons of different NLP architectures, you can make more informed decisions when selecting the right architecture for your needs.

Whether you're scaling up for real-world applications, striving for interpretability, or managing resources efficiently, the world of NLP architecture offers both challenges and opportunities. As you navigate this landscape, you'll find that the right choice of architecture can unlock the full potential of NLP in your applications, paving the way for more effective and innovative solutions.

StageZero's commitment to helping explore NLP architectures goes beyond mere encouragement. It is the core principle of our mission. We acknowledge that working with NLP can raise many questions and curiosities about the complex world of language models, algorithms, and applications. Whether you are an experienced practitioner or just starting your NLP journey, our team will support you every step of the way.

In a rapidly evolving field like NLP, it's important to stay informed and connected. Contact us to get direct access to NLP experts and resources - we offer customized insights and guidance to help you navigate the complexities of NLP.

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