Subtokenization

Subtokenization is a technique used in natural language processing (NLP) to break down words into smaller subword units, known as subtokens. This approach is particularly useful for handling out-of-vocabulary words, rare words, and morphological variations in languages. By breaking words into subtokens, models can better generalize and understand the meaning of words that were not seen during training.

One of the most common subtokenization methods is Byte Pair Encoding (BPE), which iteratively merges the most frequent pairs of characters or subtokens in a corpus. This process creates a vocabulary of subtokens that can represent a wide range of words, even those not present in the training data. Another popular method is WordPiece, used by models like BERT, which is similar to BPE but uses a different merging strategy.

Subtokenization has several advantages. It allows models to handle rare and out-of-vocabulary words more effectively, as they can be represented by combinations of known subtokens. This can improve the performance of NLP models on tasks such as machine translation, text classification, and named entity recognition. Additionally, subtokenization can help models better capture the meaning of words with morphological variations, such as plurals and verb tenses.

However, subtokenization also has some limitations. It can increase the complexity of the model, as it requires additional processing to split words into subtokens and recombine them for output. Additionally, subtokenization can sometimes lead to ambiguous representations, as the same subtoken can appear in multiple words with different meanings. Despite these challenges, subtokenization remains a valuable technique in NLP, enabling models to handle a wider range of linguistic phenomena and improve their overall performance.