Description: Subword tokenization is a technique used in natural language processing that involves breaking down words into smaller units known as subwords. This methodology is particularly useful for handling words that are not included in a model’s vocabulary, known as “out-of-vocabulary” (OOV) words. By decomposing words into subcomponents, language models can better generalize and understand morphological variations, allowing for greater flexibility in text interpretation. For example, the word “incomprehensible” can be broken down into “in-“, “comprehens”, and “-ible”, enabling the model to recognize and process parts of the word even if it has not encountered the complete word during training. This technique not only improves vocabulary coverage but also optimizes model efficiency by reducing the number of tokens it needs to handle. Subword tokenization has become an essential component in the development of large language models, such as BERT and GPT, where precise language understanding is crucial for tasks like machine translation, sentiment analysis, and text generation.
History: Subword tokenization began to gain attention in the 2010s, particularly with the development of more complex language models. An important milestone was the introduction of Byte Pair Encoding (BPE) in 2016, which provided an efficient way to tokenize text by combining the most frequent pairs of characters into subwords. This approach was utilized in models like the Transformer, which revolutionized the field of natural language processing. Since then, the technique has evolved and been integrated into various language models, enhancing their ability to handle different languages and dialects.
Uses: Subword tokenization is primarily used in training language models, allowing models to handle extensive and complex vocabularies. It is applied in tasks such as machine translation, where understanding morphological variations of words is crucial. It is also used in text recommendation systems, chatbots, and virtual assistants, where precise language understanding is fundamental. Additionally, this technique is useful in creating multilingual models, as it enables models to learn more effectively from different languages.
Examples: An example of subword tokenization is the use of BPE in the GPT-2 model, where words like “incomprehensible” are broken down into subwords such as “in-“, “comprehens”, and “-ible”. Another case is the BERT model, which uses a variant of subword tokenization to handle extensive vocabularies and improve context understanding in complex sentences.
