Deep Learning for Natural Language Processing, Subword Tokenizer

Date: YYYY-MM-DD

Author: Author Name

1. Introduction

Natural language processing is a technology that allows computers to understand and process human language. In recent years, deep learning-based natural language processing techniques have made significant advancements, particularly large language models like BERT and GPT have achieved remarkable successes. However, the performance of these models can vary greatly depending on the training data, and they require large amounts of language data. What is needed in this context is a tokenizer. This article aims to explain the principles and implementation methods of Subword Tokenizers in detail.

2. Role of Tokenizers in Natural Language Processing

A tokenizer is responsible for splitting the input sentence into smaller units called tokens. This is an essential process to convert the sentences into a form that the model can understand. Common types of tokenizers include:

  • Word-based Tokenizer: Splits based on words.
  • Byte-based Tokenizer: Splits the text into byte units.
  • Subword Tokenizer: Divides words into even smaller units for processing.

Subword tokenizers are particularly noted for their ability to address the issue of rare words. This approach allows models to handle unknown words and achieve better performance with a smaller amount of data.

3. Principles of Subword Tokenizers

A subword tokenizer is a technique that divides words into meaningful smaller parts (subwords), with the most widely used methods being BPE (Byte Pair Encoding) and WordPiece.

3.1. Byte Pair Encoding (BPE)

BPE is a method derived from compression algorithms, initially starting with individual characters and repeatedly merging the most frequently occurring character pairs to create new tokens. This process is repeated up to a specific criterion (e.g., up to 0.1% of the total number of words).

3.2. WordPiece

WordPiece is a model developed by Google that is similar to BPE but uses different hyperparameters to more finely split words. It primarily generates tokens based on frequency, giving priority to frequently occurring subword combinations. This method helps reduce the vocabulary size of the model and enhances its ability to generalize to low-frequency words.

4. Advantages of Subword Tokenizers

Subword tokenizers offer several advantages:

  • Handling Rare Words: Rare words are split into subword units, allowing the model to understand those words.
  • Reduced Vocabulary Size: Compared to a word-level approach, it decreases vocabulary size, reducing memory usage and increasing training speed.
  • Cross-Language Generalization: By using common subwords in models that handle multiple languages, knowledge can be transferred across different languages.

5. Implementation of Subword Tokenizers

Subword tokenizers can be implemented using the Python tokenizers library or the huggingface/transformers library. Below is a simple example using BPE:

from tokenizers import ByteLevelBPETokenizer

# Training data
data = ["Deep learning is a subset of machine learning.",
        "Natural language processing enables machines to understand human language."]

# Initialize tokenizer
tokenizer = ByteLevelBPETokenizer()

# Train on data
tokenizer.train_from_iterator(data)

# Tokenization
encoded = tokenizer.encode("Deep learning is fun!")
print(encoded.tokens())  # Generated tokens
print(encoded.ids())     # Token IDs

The above code first initializes the ByteLevelBPETokenizer, then trains the subwords from the data and shows the process of tokenizing a specific sentence. During this process, the subword vocabulary can be checked through the terminal or web interface.

6. Conclusion

Subword tokenizers play an important role in modern natural language processing, contributing to maximizing the performance of deep learning models. Choosing the appropriate tokenizer based on the characteristics of the data and the model’s objectives will be the foundation for building a successful natural language processing system. It is expected that subword tokenizers will continue to evolve alongside advancing deep learning technology.

7. References

  • [1] Vaswani, A., et al. (2017). Attention Is All You Need. NIPS.
  • [2] Radford, A., et al. (2019). Language Models are Unsupervised Multitask Learners. OpenAI.
  • [3] Kudo, T. (2018). Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates. arXiv preprint arXiv:1804.10959.
  • [4] Sennrich, R., et al. (2016). Neural Machine Translation of Rare Words with Subword Units. arXiv preprint arXiv:1508.07909.