Vocabulary Word

Vocabulary word research centers on handling words absent from a model's training data (out-of-vocabulary or OOV words), a critical challenge across various natural language processing tasks. Current efforts focus on improving OOV handling in machine translation, speech recognition, and text generation through techniques like data augmentation (creating synthetic data with OOV words), sub-word tokenization (breaking words into smaller units), and contrastive learning (improving model robustness to unseen words). These advancements are crucial for building more robust and generalizable language models, impacting applications ranging from improved machine translation of low-resource languages to more accurate speech recognition systems.