Subword Tokenization is an algorithm used in natural language processing (NLP) to decompose words into smaller, manageable subword units. Originally developed by Google researchers, this approach addresses the challenge of out-of-vocabulary words by creating a vocabulary that includes frequently occurring subwords. This method enhances the efficiency and effectiveness of language models by allowing them to understand and generate text more flexibly.
One prominent implementation of subword tokenization is the Byte Pair Encoding (BPE) method, which builds a vocabulary based on the frequency of subword combinations. This technique is particularly beneficial for models like BERT and other transformer architectures, improving their performance on a variety of NLP tasks by enabling them to better handle diverse language patterns and rare words.