BERT Input Representation: A Comprehensive Guide

Before BERT can effectively process textual data, the raw input must be transformed into a numerical format that the model can understand. This transformation involves a series of embedding steps, meticulously designed to capture crucial information about the token content, sentence structure, and token position within a sequence.

Step-by-Step Breakdown of BERT’s Input Representation

The preparation of input for BERT follows a specific sequence of operations:

1. Tokenization The initial step involves breaking down the input text into smaller units called tokens. For BERT, this process often uses a WordPiece tokenizer. Crucially, special tokens are added to structure the input appropriately for the model's architecture and task requirements. These special tokens include:

   • [CLS] (Classification): This token is prepended to every input sequence. The final hidden state corresponding to the [CLS] token is often used as the aggregate sequence representation for classification tasks.
   • [SEP] (Separator): This token is used to demarcate different segments within the input. For single-sentence inputs, it's placed at the end of the sentence. For sentence-pair inputs (e.g., question-answering, natural language inference), it separates the two sentences.

   Example: Input Text: "How BERT works?" Tokenized Sequence: [CLS] How BERT works ? [SEP]

2. Token Embedding Each token in the tokenized sequence is then passed through a token embedding layer. This layer maps each unique token to a fixed-dimensional vector. These initial token embedding vectors are typically initialized randomly and are learned and refined during the model's training process. The token embedding aims to capture the semantic meaning of individual words.

   Example: [CLS] -> [0.1, 0.2, ..., 0.9] (vector of dimension 768 for BERT-base) How -> [0.3, -0.1, ..., 0.5] BERT -> [-0.2, 0.4, ..., -0.7] ... and so on for each token.

3. Segment Embedding Segment embeddings are employed to distinguish between different sentences or segments within the input sequence, particularly when dealing with tasks involving multiple text inputs (e.g., sentence-pair classification). Tokens belonging to the first segment (e.g., the question in a Q&A pair) are assigned one segment ID (typically 0). Tokens from the second segment (e.g., the answer) are assigned a different segment ID (typically 1).

   Example: Input Sequence: [CLS] What is BERT ? [SEP] BERT is a language model. [SEP] Segment IDs: [0 0 0 0 0 0 1 1 1 1 1 1 1]

4. Position Embedding To enable BERT to understand the order of tokens within a sequence, a position embedding is added. This layer assigns a unique embedding vector to each specific position in the sequence (e.g., position 0, 1, 2, ...). This allows BERT to learn positional information and understand the sequential structure of the text. Unlike some recurrent models that implicitly capture order, BERT requires explicit positional encoding.

   Example: Input Sequence: [CLS] How BERT works ? [SEP] Positions: 0 1 2 3 4 5 Position IDs: [0, 1, 2, 3, 4, 5] (These IDs are then mapped to learned position embedding vectors)

Combining the Embeddings

The final enriched input representation for each token is generated by summing its corresponding token embedding, segment embedding, and position embedding. This combined embedding effectively encapsulates the token's semantic meaning, its positional information within the sequence, and the segment it belongs to.

The formula for the final input embedding is:

Final Input Embedding = Token Embedding + Segment Embedding + Position Embedding

This multi-faceted input embedding is then passed to the first encoder layer of the BERT model, providing it with a comprehensive understanding of each token's context.

Key Concepts and Special Tokens

• [CLS] Token: Used as an aggregate representation for classification tasks.
• [SEP] Token: Used to delineate segments in sentence-pair inputs.

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Interview Questions

• What are the main components of BERT’s input representation?
• Why are special tokens like [CLS] and [SEP] important in BERT?
• How does BERT’s token embedding layer work?
• What is the purpose of segment embeddings in BERT?
• How are position embeddings used in BERT and why are they necessary?
• Explain how BERT combines token, segment, and position embeddings.
• How does BERT handle sentence pairs using embeddings?
• What does the final input embedding represent in BERT?
• Can BERT work without segment embeddings? When?
• Describe the overall process of preparing raw text input for BERT.