BERT Input Data Representation

Before the BERT model can process input text, it must be converted into a numerical format. This conversion is achieved through a combination of embedding layers. BERT utilizes three key embedding types to represent input data effectively:

• Token Embeddings
• Segment Embeddings
• Position Embeddings

These embeddings are summed together to form the final input representation, which is then fed into the BERT encoder layers.

1. Token Embeddings

Token embeddings are the foundational elements of BERT's input representation. The process begins with tokenizing the input text, typically using WordPiece tokenization. Each resulting token (which can be a word or a sub-word) is then mapped to a unique numerical ID. These IDs are used to retrieve their corresponding fixed-size vectors from the token embedding matrix.

For example, the sentence "Natural Language Processing" might be tokenized as:

[CLS] Natural Language Processing [SEP]

Each of these tokens ([CLS], Natural, Language, Processing, [SEP]) is then converted into a dense vector representation by the token embedding layer.

2. Segment Embeddings

BERT is designed to handle tasks involving pairs of sentences, such as question answering or next sentence prediction. To distinguish between these distinct input segments (referred to as Sentence A and Sentence B), BERT employs segment embeddings.

• Tokens belonging to the first sentence (Sentence A) are assigned a segment embedding of 0.
• Tokens belonging to the second sentence (Sentence B) are assigned a segment embedding of 1.

This mechanism allows BERT to understand the relationship and boundaries between different input sentences.

3. Position Embeddings

Transformer architectures, including BERT, inherently lack a mechanism to understand the order of tokens in a sequence. To address this, BERT incorporates position embeddings. These embeddings inject information about the absolute or relative position of each token within the input sequence.

Each position in the input sequence is assigned a unique vector. By adding these position embeddings, the model can differentiate between tokens based on their order, even if the tokens themselves are identical.

Final Input Representation

The final input representation for each token is a combination of the three embedding types: token embeddings, segment embeddings, and position embeddings. This sum creates a rich representation that captures:

• The identity of each token: Via token embeddings.
• The position of each token: Via position embeddings.
• The segment to which each token belongs: Via segment embeddings.

This comprehensive input format enables BERT to perform deep contextual understanding and learn intricate relationships both within and across sentences.

SEO Keywords

• BERT input embeddings explained
• Token, segment, and position embeddings in BERT
• WordPiece tokenization in BERT
• How BERT processes input text
• BERT embedding layers architecture
• Position embeddings in transformers
• Segment embeddings for sentence pairs
• Final input representation in BERT

Interview Questions

• What are the three types of embeddings used in BERT's input representation?
• How does BERT use token embeddings to represent input text?
• Why are segment embeddings necessary in BERT?
• What role do position embeddings play in the BERT model?
• How does BERT distinguish between Sentence A and Sentence B?
• Explain how the final input representation is constructed in BERT.
• What is the purpose of using [CLS] and [SEP] tokens in BERT input?
• Why do transformers like BERT require position embeddings?
• How does WordPiece tokenization affect BERT’s token embedding process?
• Can BERT process unordered sequences without position embeddings? Why or why not?