Transformers Bert Applications Bert Sentence DomainSentence Pair Classification

Sentence Pair Classification

Learn how to use Sentence-BERT (SBERT) for sentence pair classification and semantic similarity tasks. Explore its Siamese network architecture for AI and ML.

Sentence-BERT for Sentence Pair Classification

Sentence-BERT (SBERT) is a powerful framework for tasks that involve evaluating the semantic similarity between two sentences. This documentation explores how to leverage SBERT for sentence pair classification, focusing on its typical Siamese network architecture.

Task Overview: Sentence Similarity Classification

The core task is to train a model that can determine whether a given pair of sentences expresses similar meanings. This is often framed as a binary classification problem:

Label 1: The sentences are semantically similar.
Label 0: The sentences are not semantically similar.

Sample Dataset Structure

A typical dataset for this task consists of entries, each containing:

Sentence Pair: Two distinct sentences.
Binary Label: A classification label indicating semantic similarity.

Example:

SBERT Workflow for Sentence Pair Classification

The process involves tokenizing sentences, encoding them into semantic representations, combining these representations, and finally classifying the pair.

1. Tokenization of Sentence Pairs

Each sentence in a pair is processed individually. This typically involves:

Tokenization: Breaking down the sentence into individual words or sub-word units (tokens).
Special Tokens: Adding special tokens required by the BERT model, such as:
- [CLS] at the beginning of each sequence. This token's final hidden state is often used as the aggregate representation of the entire sequence.
- [SEP] at the end of each sequence to mark its termination.

Example:

For the sentence pair:

Sentence 1: I completed my assignment.
Sentence 2: I completed my homework.

The tokenized sequences would look like:

Tokens 1: [CLS], I, completed, my, assignment, [SEP]
Tokens 2: [CLS], I, completed, my, homework, [SEP]

2. Siamese BERT Encoders

SBERT employs a Siamese network architecture. This means two identical BERT models, sharing the exact same weights, are used. Each sentence in the pair is independently fed into one of these identical encoders:

Sentence 1 → Encoder 1 (BERT)
Sentence 2 → Encoder 2 (BERT)

Each encoder processes its respective sentence and outputs a sequence of token embeddings.

3. Generating Sentence Representations

To obtain a single, fixed-size vector representation (embedding) for each sentence, SBERT applies a pooling strategy to the output token embeddings from the BERT encoder. Common pooling strategies include:

Mean Pooling: Averaging all the token embeddings in the sequence.
Max Pooling: Taking the maximum value across each dimension of the token embeddings.

Let:

S₁: The pooled vector representation (embedding) for Sentence 1.
S₂: The pooled vector representation (embedding) for Sentence 2.

These vectors S₁ and S₂ capture the semantic meaning of their respective sentences.

4. Combining Representations for Classification

To facilitate classification, the individual sentence embeddings S₁ and S₂ are combined into a single feature vector. Common combination methods include:

Concatenation: [S₁, S₂]
Element-wise Absolute Difference: |S₁ - S₂| (calculating the absolute difference between corresponding elements of the two vectors)

A comprehensive feature vector is often formed by combining these:

Feature Vector = [S₁, S₂, |S₁ - S₂|]

This combined vector is then passed through a fully connected (dense) layer. This layer is defined by a learnable weight matrix W.

Shape of W: n x k, where:
- n: The dimensionality of the combined feature vector.
- k: The number of output classes (e.g., 2 for binary classification).

The operation is typically a matrix multiplication: Output = Feature Vector * W.

5. Softmax Layer and Prediction

The output from the dense layer is fed into a softmax activation function. Softmax converts the raw output scores into a probability distribution across the classes. This distribution indicates the model's confidence for each class.

For binary classification, the output will be probabilities for "similar" and "not similar."

6. Training the Model

The model is trained using a suitable loss function, most commonly cross-entropy loss. This loss function quantifies the difference between the predicted probability distribution and the true labels of the training data.

During training:

The weights of the fully connected layer (W) are updated to minimize the loss.
The weights of the Siamese BERT encoders remain shared. This is crucial as it forces the encoders to learn general-purpose sentence representations that are useful for the downstream classification task. Fine-tuning the BERT encoders (though not strictly part of the core SBERT concept for this task) can further improve performance.

Summary of the Workflow

Tokenize: Process each sentence in the pair individually, adding [CLS] and [SEP] tokens.
Encode: Feed each tokenized sentence into a shared BERT encoder.
Pool: Apply a pooling strategy (e.g., mean pooling) to obtain fixed-size sentence embeddings (S₁, S₂).
Combine: Create a feature vector by concatenating and/or performing element-wise operations on S₁ and S₂.
Classify: Pass the combined feature vector through a dense layer followed by a softmax activation for class probabilities.
Train: Optimize the model using cross-entropy loss and backpropagation.

This methodology enables Sentence-BERT to effectively and efficiently handle sentence similarity classification tasks, making it valuable for applications such as duplicate detection, paraphrase identification, and semantic matching.

Potential Interview Questions

How does Sentence-BERT tokenize sentence pairs for classification tasks?
What is the role of the Siamese network architecture in SBERT for sentence pair classification?
How are sentence embeddings generated in SBERT for each sentence in a pair?
Explain common pooling strategies used to obtain fixed-size sentence embeddings in SBERT.
How does SBERT combine embeddings from two sentences for classification?
What is the function of the fully connected (dense) layer in SBERT’s classification head?
Why is the softmax activation function used in SBERT classification tasks?
How does cross-entropy loss facilitate training in SBERT sentence pair classification?
Why are the weights of the BERT encoders shared in the Siamese architecture?
What are typical applications of SBERT’s sentence pair classification capability?