How BERT Works: Understanding Bidirectional Contextual Embeddings

BERT (Bidirectional Encoder Representations from Transformers) is a groundbreaking natural language processing (NLP) model developed by Google. It leverages the power of the Transformer architecture, specifically its encoder component, to achieve state-of-the-art performance in a wide range of language understanding tasks. Unlike traditional NLP models that process text unidirectionally (either left-to-right or right-to-left), BERT's key innovation lies in its bidirectional approach.

The Foundation: Transformer Encoder

BERT's architecture is built entirely upon the encoder portion of the Transformer model. As introduced in prior discussions on the Transformer, the encoder takes a sequence of words as input and generates a contextualized representation (an embedding) for each word. This means each word's embedding is not static but is informed by its surrounding words.

The core difference between BERT and simpler NLP models is this bidirectional processing. BERT doesn't just look at the words that come before a given word; it simultaneously analyzes both the left and right context, allowing it to capture a much deeper and nuanced understanding of word meaning.

What Does "Bidirectional" Really Mean?

The bidirectional nature of BERT means that during the processing of a sentence, every word has the opportunity to "attend" to every other word. This is achieved through the Transformer's multi-head self-attention mechanism.

Let's consider an example:

Example Sentence A:

He got bit by Python.

When this sentence is fed into BERT, the multi-head self-attention mechanism within the encoder layers allows each word to consider its relationship with all other words. For instance, to understand the word "Python," BERT would analyze its interaction with words like "got" and "bit." This full-sentence awareness is crucial for disambiguating meaning. In this specific sentence, BERT can infer from the context of "bit" and the overall sentence structure whether "Python" refers to the snake or the programming language.

The output of each encoder layer is a contextualized embedding for each word. If the encoder layer size is 768 (as in BERT Base), then each word is represented by a 768-dimensional vector that encapsulates its meaning within that particular sentence.

• The embedding of 'He' is denoted as $E_{\text{He}}$
• The embedding of 'got' as $E_{\text{got}}$
• The embedding of 'Python' as $E_{\text{Python}}$
• And so on...

BERT Architecture: Stacked Encoders

BERT is not a single encoder layer but rather a stack of multiple encoder layers. While each layer performs the fundamental task of contextualizing words through self-attention, stacking them incrementally deepens the model's understanding of language.

• BERT Base: Composed of 12 encoder layers.
• BERT Large: Composed of 24 encoder layers.

With each successive layer, the word embeddings are further refined, enabling BERT to better capture subtle meanings, complex dependencies, and long-range relationships within the text.

Let's revisit the example with a different sentence:

Example Sentence B:

Python is my favorite programming language.

In this context, BERT will analyze how "Python" interacts with terms like "programming" and "language." This allows it to correctly determine that "Python" refers to the coding language. The output for this sentence will again be a set of contextualized embeddings, with each word's representation tailored to its specific meaning in this context.

Summary: How BERT Generates Contextual Representations

• Transformer Encoder Foundation: BERT exclusively uses the encoder component of the Transformer architecture.
• Bidirectional Processing: It processes input text bidirectionally, considering both the preceding and succeeding words for context.
• Multi-Head Self-Attention: Each word's representation is influenced by its relationship with every other word in the input sequence, facilitated by multi-head self-attention.
• Contextual Embeddings: The end result is a set of dynamic, contextualized embeddings that accurately reflect the precise meaning of words based on their usage within a given sentence.

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Interview Questions

• What is the architecture of BERT, and how is it different from traditional NLP models?
• What role does the encoder play in BERT’s architecture?
• How does BERT achieve bidirectional context understanding?
• What is multi-head self-attention, and how is it used in BERT?
• Why does BERT use only the encoder stack from the Transformer model?
• Explain how BERT generates contextual embeddings for words.
• How do BERT Base and BERT Large differ in terms of architecture?
• What is the significance of 768-dimensional embeddings in BERT Base?
• How does BERT refine word embeddings across its stacked encoder layers?
• In what ways does BERT handle polysemy (multiple meanings of a word) effectively?