Technical
August 15, 2023

BERT: The Start of Modern NLP

Delve deep into the groundbreaking impact of BERT on the evolution of Large Language Models. Understand how it works, compare it to its peers, and learn how it helped to change how we work.
BERT: The Start of Modern NLP

As we dive deeper into the future of technology, one phrase that often pops up in discussions is Natural Language Processing (NLP). This rapidly evolving subset of artificial intelligence (AI) has a transformative impact on businesses, specifically due to the use of and continued advancement of language models.

Specifically, one model, Google’s BERT, has created waves in the AI landscape.

What Is BERT?

BERT is a pre trained model that was designed by Google to perform natural language processing tasks like understanding and generating human language. It is an advanced example of machine learning models and stands for Bidirectional Encoder Representations from Transformers (BERT). 

Bidirectional refers to the model’s ability to simultaneously read text on both sides of a text input. Earlier, a language model could only read text inputs left to right or right to left, but BERT’s bidirectional ability allows it to grasp the nuances and complexities of inputs much better than its predecessors.

Similarly, Encoder Representations from Transformers refer to BERT leveraging the transformer model architecture for encoding representations of words. 

Understanding the Transformer Model Architecture

Previously, a language model like Recurrent Neural Networks (RNNs) or Long Short-Term Memory (LSTM) used to process text sequentially. This made it difficult for such models to handle long-range dependencies between words. 

However, the transformer model changed this by introducing the concept of "attention". This allowed a language model to now focus on different parts of the input sequence when producing an output, thereby better capturing the context and relationship between words in a text. 

The transformer architecture comprises of two main parts, which are the encoder and the decoder. While the encoder reads the entire sequence of words in an input at once rather than sequentially, the decoder does not. 

Because the BERT model only uses an encoder, it makes it truly bidirectional. This bidirectionality allows BERT to learn from the context of the words before and after a specific word, leading to a richer understanding and representation of the word.

BERT: A Masked Language Model

To fully understand BERT, it is necessary to understand the BERT architecture. While BERT is one of the first transformer based language models for natural language processing, it is also regarded as a masked language model (MLM). 

What is a Masked Language Model? 

A masked language model involves training models on sentences containing ‘masked’ tokens. In other words, it simply means training the model on sentences with hidden or masked words. The model needs to then predict the masked token or hidden word by considering the surrounding sentence context. 

To illustrate this, let us consider an example BERT training process. 

Consider that the input text is: “BERT is a groundbreaking machine learning model that has [revolutionized] our approach to natural language processing tasks”.

BERT’s task would be to predict the masked word “revolutionized”. As it is bidirectional, it can consider the surrounding context to accurately engage in sentence prediction. 

Contrasting BERT with Other Models

BERT vs. GPT

In the natural language processing landscape, GPT (Generative Pretrained Transformer) and BERT are both highly influential models. But, how do they differ? 

BERT GPT
Bidirectional in nature. It can process text both left-to-right and right-to-left. BERT uses the Encoder segment of the Transformer model. Autorefressive in nature. It is unidirectional. The text is processed in one direction. GPT uses the Decoder Segment of the Transformer model.
Applied in Google Docs, Gmail smart compose, enhanced search, voice assistance, analyzing customer reviews, etc. Applied in building applications, generating ML code, websites, writing articles and podcasts, generating legal documents, etc.
General Language understanding Evaluation (GLUE) score 80.4% and 93.3% accuracy on the SQUAD dataset. Obtained a score of 64.3% accuracy on the 'TriaAQ' benchmark and 76.2% accuracy on 'LAMBADA', with zero-shot learning.
Uses two unsupervised tasks together (Masked language modeling - fill in the blanks and Next Sentence prediction - does sentence B comes after Sentence A) Text generation is straightforward using autoregressive language modeling.

Source: 360DigiTMG

While BERT is a bidirectional language model, GPT is unidirectional. This difference impacts their abilities. On the one hand, BERT would excel at tasks involving understanding context and sentence prediction and structures. On the other hand, GPT would shine in text generation tasks. Rather than one being “better than the other”,  the choice between BERT models and GPT models depends on the specific NLP task at hand.

BERT vs. LSTM

As visited earlier, Long Short-Term Memory (LSTM) is another neural model that belongs to the family of recurrent neural networks (RNNs). It is particularly effective in handling sequential data tasks like in the fields of finance for stock price prediction or in the field of medicine for DNA sequencing. 

However, LSTM has limitations in capturing long-range dependencies within text due to its sequential nature. But as we know, BERT models can simultaneously process all parts of a sentence due to its adaptations from the Transformer model. This makes them more effective in capturing the entire context for NLP tasks.

BERT vs. Traditional Language Models

Traditional Language Models like N-gram Models or single direction language models, typically predict the next word in a sentence based on the previous words. They lack the ability to consider the entire context of a sentence, leading to limitations in language understanding and sentence prediction. But BERT models can effectively consider the context from both directions, and hence understand language nuances more precisely.

The Applications of BERT

Here’s a quick overview of the nine popular applications of BERT.

Google and BERT

Primarily, Google famously uses BERT in its search engine to understand search queries better and provide more relevant results. With BERT, Google Search can better understand the context of words in a search query, particularly for more conversational or nuanced queries. It acts as a testament to BERT's effectiveness in understanding human language in real-world applications.

Yet, the BERT model has numerous applications beyond Google Search and it is especially versatile and powerful for NLP tasks.

Text Prediction

One of the fundamental uses of the BERT model is text prediction. In text prediction tasks, the model must predict a masked word based on its context. This is part of BERT's pre-training, where it uses training data to learn the context of a word relative to its surrounding words. BERT models can then predict what a masked word in a sentence might be and use its next sentence prediction capabilities not only in Google Searches, but also in Chatbot Support or Grammar Checking softwares like Grammarly.

  • Applications in Business: Customer Support Chatbots. 

As a business owner, enhancing customer support is paramount. Implementing text prediction in your chatbots, allows them to anticipate user queries and respond more rapidly. 

  • How does BERT provide an edge? BERT’s bi-directional understanding means it doesn't just predict the next word, but comprehends the surrounding context. This leads to more accurate suggestions in chatbot and more contextually appropriate and precise responses, heightening customer satisfaction and notable reduction in customer service-related costs.

Text Generation

While BERT's primary design is not for text generation, its deep understanding of language context allows it to be used for this purpose. By predicting the likelihood of subsequent words, it can generate sentences that are contextually relevant.

  • Applications in Business: Content Creation for Marketing Campaigns

Marketing departments are constantly under the crunch to produce fresh content. Utilizing text generation, they can produce relevant and cohesive draft articles, social media posts, or advertising copy to accelerate the content creation process. 

  • How does BERT provide an edge? Content generated in tandem with BERT's insights is more contextually relevant, engaging diverse audiences effectively. This aids in maintaining a consistent online presence, giving you a competitive edge in the digital marketing arena. 

Sentiment Analysis

In sentiment analysis, BERT's ability to understand the context of text shines. It can detect the sentiment of a sentence, paragraph, or document, helping businesses understand customer opinions and feelings about their products or services.

  • Applications in Business: Product Review Monitoring

Using sentiment analysis to scan online reviews and social media mentions provides invaluable insights into how a product is perceived. 

  • How does BERT provide an edge? By using deep sentence learning, BERT offers a more genuine sentiment assessment which allows to gauge genuine consumer feelings about products or services. This helps address concerns promptly, iterate on feedback, and understand consumer sentiment trends. 

Text Summarization

BERT's proficiency at understanding language context can also be applied to text summarization. By understanding the main points in a piece of text, BERT can generate a concise summary, thus aiding in NLP tasks such as summarizing news articles or condensing lengthy reports.

  • Applications in Business: Report Summarization and Executive Briefs

As a CEO, time is valuable. Using text summarization tools condense lengthy market research and data heavy documents into concise executive briefs, granting executives the essence of the content without the fluff. 

  • How does BERT provide an edge? As BERT captures key information within texts, it makes summaries more informative and representative of the original content which allows for more informed decision making. 

Text Classification

BERT can be fine-tuned for text classification tasks such as categorizing emails or news articles into predefined categories. Its ability to understand the context of an entire sentence or document aids in classifying text based on content.

  • Applications in Business: Automated Email Sorting

If you're heading a firm inundated with client emails daily, text classification can help automatically categorize emails into different buckets such as 'Urgent', 'Client Feedback', or 'Invoice Queries'. 

  • How does BERT provide an edge? BERT's context-rich encoding helps categorize emails or documents more accurately based on nuanced content interpretations, allowing for higher accuracy and ensuring that important communications are attended to promptly.

Question Answering

Question answering tasks involve the model understanding a question posed in natural language and finding the correct response from a given text. BERT excels at this by understanding the question in context and accurately extracting the answer from the text.

  • Applications in Business: Internal Knowledge Bases

Consider running a multinational corporation with vast internal resources. Implementing a question answering system for the internal knowledge base will enable employees to fetch specific information swiftly and optimize use of resources. 

  • How does BERT provide an edge? BERT's deep understanding of context leads to accurate extraction of answers from vast knowledge bases, even if questions are paraphrased or nuanced. This allows employees to get precise answers, reducing time spent on information retrieval. Further, it not only improves employee efficiency but also ensures that institutional knowledge is easily accessible and fosters a culture of continuous learning.

Named Entity Recognition

Named Entity Recognition (NER) involves identifying proper nouns in text, such as names of people, organizations, locations, etc. BERT can be fine-tuned for NER tasks, contributing to applications like automatic news tagging or content recommendation systems.

  • Applications in Business: Automated Legal Document Analysis

Given that legal documents use complex language and are structured differently, integrating NER can help scan legal documents to identify and categorize entities such as names, organizations, or dates. This aids in rapidly processing information, ensuring that no critical detail is missed and reduces manual review hours.

  • How does BERT provide an edge? The bidirectional context-awareness of BERT recognizes entities with greater precision, even in complex legal sentence structures. This ensures meticulous parsing of all legal documents with heightened accuracy and safeguards against oversight.

Machine Translation

While not a primary application of BERT, it can be utilized in machine translation tasks due to its understanding of context. Combining BERT with other models such as sequence-to-sequence models can lead to improved translation accuracy.

  • Applications in Business: Global Business Expansion

To grow, businesses have to expand into new markets that may have additional barriers due to different consumer tastes, preferences and behavior. One such common barrier is a language barrier. Utilizing machine translation can help quickly translate business materials, websites, and communications for different regional markets.

  • How does BERT provide an edge?  While BERT isn't a language translation tool per se, its contextual understanding can augment translation models, ensuring translated content retains the original sentiment. This facilitates smooth entry into new regions, ensuring that language isn't a barrier to your global ambitions.

With its powerful language understanding capabilities, BERT represents a significant advancement in machine learning models for NLP tasks. Its bidirectional nature and focus on context has resulted in a model that can handle a wide range of NLP tasks with state-of-the-art results. As we continue to improve and build upon models like BERT, the future of NLP looks bright, and we at Multimodal are excited to be part of this journey.

How to Create a BERT Model

Creating a BERT model involves a two-step process of pre-training and fine-tuning. The pre-training stage involves training the BERT model on a large corpus of text, such as Wikipedia. This is where BERT learns language context, relationships between words, and other nuances of human language through tasks like masked language modeling and next sentence prediction. After pre-training, we obtain a pre-trained BERT model.

The second step involves fine-tuning, where the pre-trained BERT model is customized for specific tasks. This involves training the model on a specific dataset relevant to the task, such as question answering or sentiment analysis. This stage leverages transfer learning, where knowledge gained during pre-training is applied to the new task. Fine-tuning adjusts the weights of the pre-trained BERT model, effectively tailoring it to the task at hand.

BERT and Your Business

At Multimodal, we harness the power of BERT and other advanced models to offer services like text generation, text summarization, text classification, and document processing. Our goal is to make these advanced AI tools accessible and beneficial to businesses of all sizes. We believe that with the right tools, every business can make better decisions, increase efficiency, and improve customer engagement.

Whether you're a small business, a startup, or a larger enterprise, integrating AI like BERT into your processes can provide a competitive edge and drive innovation. If you're excited about the possibilities, reach out to us at Multimodal. Let's explore how we can leverage these advanced models to make your business more efficient and successful.

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