- Jun 30
- 4 min read

Subword Regularization with BPE

Overview

Subword regularization is a technique used to improve the robustness and generalization capabilities of language models by introducing variability in the tokenization process. It allows models to consider multiple possible tokenizations of a word, enhancing their ability to handle unseen words or phrases. This approach is particularly useful in handling rare words, morphological variations, and improving the model's performance across different domains.

Subword Regularization Workflow

Vocabulary Creation (Learning Phase)

Corpus Analysis:
1. The process begins with analyzing a large text corpus to understand the frequency of character pairs.
Initial Tokenization:
1. Initially, each word in the corpus is broken down into individual characters.
2. Example: The word "quick" is represented as ['q', 'u', 'i', 'c', 'k'].
Frequency Counting:
1. Count the frequency of all adjacent character pairs in the corpus.
2. Example: If "qu" appears frequently, it is identified as a candidate for merging.
Iterative Merging:
1. Merge the most frequent pairs of characters or subwords to form new subwords. This step is repeated until the desired vocabulary size is reached.
2. Example Merges:
  1. Merge 'q' and 'u' to form 'qu'.
  2. Merge 'i' and 'c' to form 'ic'.
  3. Merge 'qu' and 'ic' to form 'quick'.
Final Vocabulary:
1. The result is a vocabulary consisting of subwords that efficiently represent the text.
2. Example Vocabulary: ['The', 'qu', 'ick', 'br', 'own', 'fox', 'jump', 's', 'over', 'the', 'lazy', 'dog', '.']

Tokenization (Application Phase)

Input Text:
1. Take new text that needs to be tokenized.
2. Example: "The quick brown fox."
Generate Multiple Tokenizations:
1. For each word or phrase, generate all possible tokenizations using the subword vocabulary.
2. Example for "quick":
3. Possible tokenizations: ["qu", "ick"], ["qui", "ck"]
Assign Probabilities:
1. Assign probabilities to each possible tokenization based on their frequencies or other heuristics.
2. Example: "qu" might be more frequent than "qui", so ["qu", "ick"] might have a higher probability than ["qui", "ck"].
Probabilistic Sampling:
1. During training, randomly select one of the possible tokenizations for each word according to their probabilities.
2. This introduces variability in the tokenized output seen by the model during training.

Example

Sentence: "The quick brown fox jumps over the lazy dog."

Initial Tokenization:
1. Characters: ['T', 'h', 'e', ' ', 'q', 'u', 'i', 'c', 'k', ' ', 'b', 'r', 'o', 'w', 'n', ' ', 'f', 'o', 'x', ' ', 'j', 'u', 'm', 'p', 's', ' ', 'o', 'v', 'e', 'r', ' ', 't', 'h', 'e', ' ', 'l', 'a', 'z', 'y', ' ', 'd', 'o', 'g', '.']
Frequency Counting and Iterative Merging:
1. Most frequent pairs: ('T', 'h'), ('h', 'e'), ('q', 'u'), ('u', 'i'), ('i', 'c'), ('c', 'k'), ('b', 'r'), ('r', 'o'), ('o', 'w'), ('w', 'n'), ('f', 'o'), ('o', 'x'), ('j', 'u'), ('u', 'm'), ('m', 'p'), ('p', 's'), ('o', 'v'), ('v', 'e'), ('e', 'r'), ('l', 'a'), ('a', 'z'), ('z', 'y'), ('d', 'o'), ('o', 'g')
2. Iterative merging results in subwords: ['The', 'qu', 'ick', 'brown', 'fox', 'jumps', 'over', 'the', 'lazy', 'dog', '.']
Subword Regularization:
1. Generate Multiple Tokenizations:
  1. "quick": ["qu", "ick"], ["qui", "ck"]
  2. "brown": ["b", "rown"], ["br", "own"]
  3. "lazy": ["l", "azy"], ["la", "zy"]
2. Assign Probabilities:
  1. "quick": ["qu", "ick"] (70%), ["qui", "ck"] (30%)
  2. "brown": ["b", "rown"] (60%), ["br", "own"] (40%)
  3. "lazy": ["l", "azy"] (80%), ["la", "zy"] (20%)
3. Probabilistic Sampling:
4. During each training step, randomly select one of the tokenizations for each word based on the assigned probabilities.

Example Tokenizations During Training:

Iteration 1:
1. "The qu ick brown fox jumps over the lazy dog."
Iteration 2:
1. "The qui ck brown fox jumps over the lazy dog."
Iteration 3:
1. "The qu ick b rown fox jumps over the la zy dog."

By introducing this variability in tokenization, subword regularization ensures that the model sees different subword sequences during training. This helps in building a more robust model that can handle variations in text, such as typos, morphological changes, and rare words, thereby improving its generalization capability.

Pros and Cons of Subword Regularization

Pros

Improved Robustness:
1. Exposes the model to multiple tokenization patterns, enhancing its ability to handle typos, morphological variations, and rare words.
Better Generalization:
1. Increases the model’s performance on unseen data by providing varied training examples.
Handling Rare Words:
1. Breaks down rare or out-of-vocabulary words into more frequent subwords, improving the model’s understanding and generation of these words.
Reduced Overfitting:
1. Introduces noise and variability, reducing the risk of overfitting to specific tokenization patterns.

Cons

Increased Computational Complexity:
1. Managing multiple tokenization patterns and probabilistic sampling adds computational overhead during training.
Implementation Complexity:
1. Requires careful management of tokenization patterns and probabilistic sampling, adding to the implementation complexity.
Training Data Variability:
1. Introducing variability in tokenization can lead to inconsistencies in the training data, potentially confusing the model if not managed properly.
Evaluation Complexity:
1. Varying tokenizations can affect the consistency of model outputs, complicating the evaluation process.

Key Considerations

Hyperparameter Tuning:
1. Careful tuning of parameters like the number of tokenization samples and randomness factor (alpha) is crucial for optimal performance.
Training Time and Resources:
1. Be prepared for increased training times and resource usage due to the additional computational load from probabilistic sampling and multiple tokenizations.
Corpus Characteristics:
1. Ensure the corpus used for subword regularization is diverse enough to benefit from the variability introduced.