What are subword tokenization methods?

Theoretical Background:

Subword tokenization methods like BPE and WordPiece are designed to address problems such as out-of-vocabulary (OOV) words and morphological diversity in languages. These methods break down words into smaller units, or subwords, which can be recombined to form words. This allows models to process and understand rare or new words by decomposing them into known subword units.

• Byte Pair Encoding (BPE): BPE starts with a base vocabulary of all unique characters in the text. It then repeatedly merges the most frequently occurring pairs of adjacent symbols until a predefined vocabulary size is reached. This method is effective because it can capture common prefixes and suffixes, which are prevalent in many languages.

• WordPiece: Similar to BPE, WordPiece begins with all characters and merges the most frequent pairs based on a probabilistic model. However, WordPiece also considers the likelihood of subwords occurring in context, which can lead to more linguistically meaningful tokenization.

Practical Applications:

Both BPE and WordPiece are widely used in modern NLP models. For instance, BERT uses WordPiece tokenization to efficiently handle its large corpus during pretraining. These methods are also utilized in machine translation, sentiment analysis, and other NLP tasks.

Example Code:

Here's a simple example of BPE in Python using the tokenizers library:

from tokenizers import Tokenizer
from tokenizers.models import BPE
from tokenizers.trainers import BpeTrainer
from tokenizers.pre_tokenizers import Whitespace

# Initialize a tokenizer
tokenizer = Tokenizer(BPE())

# Train the tokenizer on a text corpus
trainer = BpeTrainer(vocab_size=30000, min_frequency=2)
tokenizer.pre_tokenizer = Whitespace()
tokenizer.train(["path/to/corpus.txt"], trainer)

External References/Links:

• BPE Explained by OpenAI
• WordPiece Paper

Diagram:

graph TD;
    A[Input Text Corpus] -->|Initialize| B[Character Vocab];
    B -->|Iterative Merging| C[Subword Units];
    C -->|Create| D[Final Vocabulary];
    D -->|Tokenization| E[Model Input];

This diagram illustrates the process of converting an input text corpus into a model-ready format using subword tokenization methods.