Sub Word

Subword tokenization is a technique in natural language processing that breaks words into smaller units, improving language model efficiency and handling out-of-vocabulary words. Current research focuses on evaluating the quality of subword vocabularies, particularly their alignment with morphemes and impact on downstream tasks like hate speech detection and machine translation, often employing models like BERT and utilizing algorithms such as BPE, WordPiece, and Unigram. These advancements are significant because improved subword tokenization enhances the performance and robustness of language models across various applications, including multilingual translation and text generation, while also offering insights into cognitive processing of language.