Alternatives to subword tokenization

Subword tokenization is a technique that splits words into smaller units, such as prefixes, suffixes, or common character sequences. This allows NLP models to represent a vast vocabulary efficiently and handle unknown words by composing them from known subwords, proving highly effective in both research and practical applications.

At a glance

Executive summary

Subword tokenization breaks down words into smaller, meaningful units, allowing models to handle rare or unknown words by composing them from known subwords. This approach is crucial for modern NLP models, especially those dealing with large vocabularies and diverse languages, as it balances vocabulary size with representational power.

TL;DR

If you need a general-purpose, robust tokenization strategy for diverse text, use subword tokenization; if you need a pre-trained model that already incorporates subword tokenization and multilingual capabilities, consider Multilingual BERT.

Key points

Subword tokenization offers a flexible way to represent words, especially out-of-vocabulary terms, by breaking them into common subword units.
It helps manage vocabulary size while retaining the ability to understand novel or rare words.
The choice of subword algorithm (e.g., BPE, WordPiece, SentencePiece) can impact performance and vocabulary characteristics.
Subword tokenization is a foundational technique for many state-of-the-art NLP models, including transformers.
When working with pre-trained models, understanding their underlying tokenization strategy is essential for effective use.

Our Take

### Our Take Subword tokenization and Multilingual BERT (mBERT) represent two significant advancements in natural language processing, each with its own strengths and weaknesses. Subword tokenization, as popularized by the Byte Pair Encoding (BPE) method, allows models to handle out-of-vocabulary words by breaking them down into smaller, more manageable pieces. This approach enhances the model's ability to generalize across languages and dialects, making it particularly effective for low-resource languages (Sennrich et al., 2016). On the other hand, Multilingual BERT, which is pre-trained on a large corpus of multilingual text, employs a unique architecture that allows it to learn shared representations across multiple languages. According to Pires et al. (2019), mBERT demonstrates impressive performance on various cross-lingual tasks, outperforming models trained on individual languages. This suggests that mBERT's architecture and training strategy enable it to leverage linguistic similarities, making it a powerful tool for multilingual applications. However, while subword tokenization enhances vocabulary coverage, it may lead to inefficiencies in processing, as longer sequences of subwords can increase computational costs. Conversely, mBERT's reliance on a unified vocabulary can simplify the model's architecture but may limit its adaptability to specific languages (Devlin et al., 2019). In conclusion, the choice between subword tokenization and Multilingual BERT largely depends on the specific application and language context. For tasks requiring extensive vocabulary flexibility, subword tokenization may be preferable. However, for applications that benefit from a shared multilingual framework, mBERT offers a compelling solution. Ultimately, understanding the nuances of each approach can guide practitioners in selecting the most suitable method for their needs.

Alternative	Difference	Papers (with subword tokenization)	Avg viability
Multilingual BERT	—	1	—

Alternative

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Papers (with subword tokenization)

Avg viability

Multilingual BERT

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