Segment Embeddings in BERT

Segment embeddings are a fundamental component of BERT's architecture, enabling it to effectively process and understand relationships between multiple sentences. They serve as a mechanism to distinguish between individual sentences when they are presented together as input, which is crucial for tasks like sentence pair classification, natural language inference, and question answering.

What are Segment Embeddings?

When BERT receives two sentences as input, it needs a way to differentiate which tokens belong to the first sentence and which belong to the second. Segment embeddings provide this distinction by assigning a specific embedding to each token based on its sentence origin.

Example: Segment Embeddings with Sentence Pairs

Consider the following two sentences:

Sentence A: Paris is a beautiful city. Sentence B: I love Paris.

After tokenization and the addition of special tokens, the input sequence to BERT might look like this:

tokens = [ [CLS], Paris, is, a, beautiful, city, [SEP], I, love, Paris, [SEP] ]

Here, [CLS] is a special token prepended to the entire input, and [SEP] is used to separate the two sentences. While [SEP] marks the boundary, segment embeddings provide an explicit signal to the model about which sentence each token belongs to.

How Segment Embeddings Work

BERT utilizes two distinct, fixed segment embeddings:

• Segment A Embedding: Typically assigned a value of 0.
• Segment B Embedding: Typically assigned a value of 1.

The assignment process is as follows:

1. Sentence A Tokens: Every token originating from Sentence A, including the initial [CLS] token and the first [SEP] token, is mapped to the Segment A embedding.
2. Sentence B Tokens: Every token originating from Sentence B, including the second [SEP] token, is mapped to the Segment B embedding.

By combining these segment embeddings with token embeddings (representing the words themselves) and position embeddings (representing the order of tokens), BERT constructs a rich input representation that allows it to learn complex relationships and dependencies between the sentences.

Segment Embeddings with Single Sentence Input

When BERT processes a single sentence as input, all tokens in that sequence are assigned to Segment A (segment ID 0). Since there is no second sentence to differentiate from, the Segment B embedding is not utilized.

Summary

Segment embeddings are binary embeddings within BERT that serve to disambiguate between multiple sentences within a single input sequence. They are a critical component that, when combined with token and position embeddings, forms the comprehensive input representation for the model. This mechanism is essential for BERT's ability to understand inter-sentence relationships, making it highly effective for tasks such as:

• Natural Language Inference (NLI)
• Question Answering (QA)
• Sentence Pair Classification

Technical Details and Related Concepts

• Segment IDs: The numerical identifiers (0 for Segment A, 1 for Segment B) used to select the appropriate segment embedding.
• Input Representation: The final vector representation of a token is typically the sum of its token embedding, position embedding, and segment embedding.
• BERT Input Encoding: The process of preparing text for BERT, which includes tokenization, adding special tokens, and generating segment and position embeddings.
• [CLS] Token: While often used for classification tasks by taking its final hidden state, its segment embedding reflects the sentence it belongs to (Sentence A in a pair).
• [SEP] Token: Used to delineate sentences. The first [SEP] is assigned to Segment A, and the second [SEP] is assigned to Segment B.

Interview Questions

• What are segment embeddings in BERT and what is their primary purpose?
• How does BERT differentiate between two input sentences using segment embeddings?
• What are the typical segment IDs used in BERT for sentence pairs, and how are they assigned?
• Describe how segment embeddings are applied when BERT receives only a single sentence as input.
• Why are segment embeddings particularly important for tasks like question answering?
• What segment ID is assigned to the [CLS] token when processing a sentence pair?
• To which segment embedding (A or B) are [SEP] tokens assigned in BERT?
• How do segment embeddings contribute to BERT's understanding of inter-sentence relationships?
• Can BERT operate effectively without segment embeddings? In which scenarios or tasks might they be omitted or less critical?
• Explain how segment embeddings are combined with token embeddings and position embeddings to create the final input representation for BERT.