Yes, contextualized word embeddings have the ability to handle out-of-vocabulary (OOV) words. Contextualized word embeddings, such as those generated by models like BERT, ELMo, and GPT, are trained on large amounts of text data and learn to capture the context and meaning of words based on the surrounding words in a given sentence or document.

  When encountering an OOV word, contextualized word embeddings can still provide meaningful representations by leveraging the contextual information from the surrounding words. These models use a pre-training step where they are trained on a large corpus of text data, learning to predict missing words or next words in a given sentence. This process enables the models to develop a sense of context and semantic understanding.

  During training, contextualized word embeddings learn to generate word representations that capture various linguistic aspects, including the syntax, semantics, and context of the words. As a result, when an OOV word is encountered during inference or prediction, the model can use the contextual information of the surrounding words to generate a meaningful representation for the OOV word based on the learned patterns and context from the training data. This allows the model to handle and understand OOV words to some extent.

  However, it is important to note that the contextualized word embeddings might not be able to provide accurate representations for extremely rare or unseen words, as they heavily rely on the patterns and context of words learned during training. For such cases, additional techniques like character-level embeddings or handling of unknown words explicitly may be required to handle the OOV words effectively.