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 In the realm of natural language processing, the choice between subword tokenization and models like Multilingual BERT (mBERT) is pivotal for achieving optimal performance across diverse languages. Subword tokenization, as introduced in the Byte Pair Encoding (BPE) method, allows for the decomposition of words into smaller, more manageable units. This approach effectively handles out-of-vocabulary (OOV) words and aids in capturing morphological variations, which is particularly beneficial for morphologically rich languages (Sennrich et al., 2016). On the other hand, Multilingual BERT leverages a pre-trained transformer architecture that is designed to work across multiple languages without needing language-specific adaptations. According to Pires et al. (2019), mBERT performs surprisingly well on zero-shot cross-lingual tasks, demonstrating its ability to generalize knowledge across languages. However, its reliance on a fixed vocabulary can sometimes limit its performance on less-represented languages or dialects, where subword tokenization may have an edge. The evidence suggests that while mBERT excels in scenarios requiring cross-lingual understanding, subword tokenization provides a more granular approach to language representation. For instance, a study by Kudo and Richardson (2018) indicates that subword models consistently outperform traditional word-based models in various NLP tasks, particularly in languages with rich morphology. Ultimately, the choice between subword tokenization and mBERT depends on the specific requirements of the task at hand. For applications demanding robust handling of diverse linguistic structures, subword tokenization may offer superior flexibility, while mBERT remains an excellent choice for tasks emphasizing cross-lingual transferability. Balancing these methodologies could lead to even more powerful multilingual models in the future.

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

Alternative

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Papers (with subword tokenization)

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Multilingual BERT

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