Tokenization is a crucial step in natural language processing, traditionally handled through word or character level methods. While word level tokenization can lead to unknown tokens for infrequent words, character level tokenization requires many tokens and lacks inherent meaning. Subword tokenization emerges as a balanced solution, combining the efficiency of word tokens with the flexibility to manage out-of-vocabulary words, enhancing model performance.
