Compute Sentence Representations

Computing Sentence Representations with BERT

Understanding sentence representation is crucial for numerous Natural Language Processing (NLP) tasks, including semantic similarity, sentiment analysis, and question answering. This document explores how to compute sentence representations using a pre-trained BERT model and introduces strategies to enhance accuracy.

1. Tokenization and Special Tokens

To begin processing a sentence with BERT, we first tokenize it and append special tokens required by the BERT architecture.

Example Sentence: "Paris is a beautiful city."

Tokenization Process: BERT requires two special tokens:

• [CLS] (Classification Token): Added at the beginning of the token sequence. This token's final hidden state is typically used as the aggregate representation for classification tasks.

• [SEP] (Separator Token): Added at the end of the token sequence, and also between sentence pairs if the input consists of multiple sentences.

Resulting Tokenized Sequence:

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

2. Passing Tokens Through BERT

The tokenized sequence is then fed into a pre-trained BERT model. BERT processes each token, generating a high-dimensional vector representation for every token in the sequence. This process results in contextualized embeddings.

Mathematically, if tokens is the input sequence and BERT is the pre-trained model: $$R_i = \text{BERT}(\text{tokens})$$ Where $R_i$ is the contextualized embedding for the $i$-th token. Each token's embedding captures its semantic meaning based on its context within the entire sentence.

3. Using the [CLS] Token as Sentence Representation

Traditionally, the contextualized embedding of the [CLS] token (denoted as $R_{[CLS]}$) is often considered the aggregate representation of the entire sentence. This vector is frequently used as the sentence-level embedding for downstream tasks.

Limitation: However, if you are using a pre-trained BERT model without fine-tuning on a specific downstream task, the [CLS] token's representation may not accurately capture the full semantics of the sentence. This limitation has prompted the exploration of more effective alternatives.

4. Alternative Approach – Pooling Strategies

Instead of solely relying on the [CLS] token, a more robust sentence representation can be derived by pooling the contextualized embeddings of all tokens in the sentence. Two popular pooling strategies are:

4.1. Mean Pooling

• Description: Calculates the average of all token vectors in the sequence.

• Benefit: Captures the overall semantic meaning of the sentence by giving equal weight to all tokens.

4.2. Max Pooling

• Description: Takes the maximum value across all token vectors for each dimension.

• Benefit: Highlights the most informative or dominant features in the sentence by focusing on the strongest activations.

By employing mean or max pooling, the resulting sentence representation becomes more robust, especially when using a BERT model directly without task-specific fine-tuning.

5. Visual Summary of Pooling-Based Sentence Representation

The process of generating a sentence embedding using pooling can be visualized as:

[CLS] | Token 1 | Token 2 | ... | Token N | [SEP] ------- | -------- | -------- | ----- | -------- | -------- R_[CLS] | $R_1$ | $R_2$ | ... | $R_N$ | $R_{[SEP]}$

Then, applying a pooling operation (Mean or Max) over these token representations ($R_1, R_2, ..., R_N$) yields a single sentence vector. This approach ensures a richer and often more accurate representation of sentence meaning compared to relying solely on the [CLS] token.

What’s Next? Understanding Sentence-BERT

Now that we've covered deriving sentence embeddings from BERT using pooling strategies, the next logical step is to explore Sentence-BERT (SBERT). SBERT is specifically designed to produce semantically meaningful sentence embeddings and significantly improves performance on tasks like semantic similarity and clustering by fine-tuning BERT using a siamese and triplet network structure.

Conclusion

Computing sentence representations with BERT involves:

1. Tokenizing the input text.

2. Adding BERT-specific special tokens ([CLS], [SEP]).

3. Feeding the tokens through a pre-trained BERT model to obtain contextualized token embeddings.

4. Deriving a sentence-level embedding either from the [CLS] token or by applying pooling strategies (mean or max pooling) over all token embeddings.

While the [CLS] token offers a convenient summary, using mean or max pooling can yield more accurate and robust sentence-level embeddings, particularly when working with BERT without task-specific fine-tuning.

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Interview Questions:

• What role do the [CLS] and [SEP] tokens play in BERT’s input format?

• How does BERT generate contextual embeddings for each token in a sentence?

• Why is the [CLS] token often used for sentence-level tasks in BERT?

• What are the limitations of using the [CLS] token for sentence embeddings without fine-tuning?

• Explain the mean pooling strategy and how it is applied to BERT embeddings.

• What is max pooling, and how does it differ from mean pooling in capturing sentence semantics?

• Which pooling strategy is generally more robust for generating sentence embeddings with BERT without fine-tuning?

• Can pooling strategies replace fine-tuning for some downstream tasks? Explain why or why not.

• What is the impact of token-level embeddings on the quality of sentence representation?

• How does BERT’s sentence embedding approach compare to Sentence-BERT?