2018: BERT – Meet the AI That Reads Context Like a Human

Introduction

2018 was a groundbreaking year for Natural Language Processing (NLP). Google introduced BERT (Bidirectional Encoder Representations from Transformers), an AI model that understands language contextually, just like humans do. Unlike earlier models that read text sequentially (left-to-right or right-to-left), BERT looks at words in both directions simultaneously. This bidirectional understanding allows it to grasp the meaning of sentences with far greater accuracy.

As someone fascinated by language and AI, I was blown away by how BERT works. Let’s dive into what makes it so revolutionary.

How BERT Works

1. Bidirectional Context

Before BERT, most NLP models, like OpenAI’s GPT, processed text in a specific direction—either left-to-right (like humans reading a book) or right-to-left. While these models worked well, they couldn’t fully understand words that depend on the context of the entire sentence.

For example, in the sentence:

“The bank will not approve the loan because it is risky.”

• Is "bank" referring to a riverbank or a financial institution?
• Earlier models struggled because they didn’t "see" the entire sentence while interpreting each word.

BERT changes the game by looking both ways. It considers the word "bank" alongside all the surrounding words—both before ("The bank will not approve...") and after ("...because it is risky"). This ability to look at context from all directions makes BERT much smarter at understanding meaning.

2. Masked Language Modeling (MLM)

One of the coolest things about BERT is how it learns using Masked Language Modeling (MLM).

Here’s how it works:

• During training, BERT randomly "masks" some words in a sentence (replaces them with a special [MASK] token).
• The task is for BERT to predict the masked words using the surrounding context.

For example:

Input: "The [MASK] will not approve the loan."

Output: "The bank will not approve the loan."

By forcing BERT to predict missing words, it learns how words relate to each other in a sentence. This is a bit like solving a jigsaw puzzle—BERT learns to "fill in the blanks" based on context clues.

3. Next Sentence Prediction (NSP)

Another task BERT uses during training is Next Sentence Prediction (NSP).

Here’s how NSP works:

• BERT is given two sentences and must decide if the second sentence logically follows the first.

Example:

Sentence A: "The sun is shining brightly."

Sentence B: "I need to buy sunscreen."

Prediction: True (Sentence B logically follows Sentence A).

By learning relationships between sentences, BERT gets better at understanding the flow of ideas, which is crucial for tasks like question answering and summarization.

BERT in Action

• Sentence Disambiguation: BERT knows "bank" refers to a financial institution, not a riverbank, because of the word "withdraw cash."
• Question Answering: BERT can answer questions like "Who wrote Harry Potter?" by identifying J.K. Rowling in a passage.
• Sentiment Analysis: BERT identifies positive tones in sentences like "I absolutely love this product!"

Why BERT is Revolutionary

1. Pretraining on Large Data: BERT is trained on a massive dataset, including Wikipedia and books, to learn the nuances of language.

2. Fine-Tuning for Specific Tasks: Once pre-trained, BERT can be fine-tuned for specific tasks like translation or classifying reviews.

3. Bidirectional Understanding: BERT’s ability to look at both past and future context makes it better than unidirectional models.

The Math Behind BERT

BERT uses the Transformer architecture, which employs self-attention to determine the importance of words in a sentence.

Self-attention formula:

Attention(Q, K, V) = softmax((QK^T) / √d_k)V

• Q: Query matrix
• K: Key matrix
• V: Value matrix
• d_k: Dimensionality of the key vectors

My Thoughts on BERT

BERT is a game-changer. It’s not just about improving NLP tasks—it’s about changing how we think about language understanding in AI.

I’ve started experimenting with BERT using Python libraries like Hugging Face Transformers. Fine-tuning BERT for tasks like classifying tweets or summarizing articles is super fun and accessible!

P.S. If you’re curious to learn more, check out Google’s original research paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.