Contextualized embeddings are a type of word representation generated by deep learning models, such as BERT, ELMo, or GPT. These embeddings capture the meaning of a word in the context of the whole sentence, allowing for a more nuanced understanding of language.

  When it comes to dealing with rare or unseen words, contextualized embeddings have some advantages over traditional word embeddings, such as word2vec or GloVe. Here's how they handle these cases:

  1. Morphological information: Contextualized embeddings take into account not only the word itself but also its surrounding context. This means that even if a word is rare or unseen, the model can still infer some information from its morphological structure or the words around it. For example, if the model has seen similar suffixes or prefixes before, it can generalize the meaning to the unseen word.

  2. Transfer learning: Contextualized embeddings are trained on large-scale corpora, which means they have been exposed to a wide range of words and contexts during training. Therefore, they can leverage the knowledge gained from this exposure to infer the meaning of rare or unseen words. This transfer learning property makes contextualized embeddings more effective in dealing with out-of-vocabulary words.

  3. Subword information: Contextualized embeddings are often built using subword units, such as character or byte-level n-grams. These subword units allow the model to handle unseen words by breaking them down into smaller meaningful components. By leveraging the similarity between subword units, the model can make educated guesses about the meaning of unseen words.

  4. Fine-tuning: After pre-training on a large corpus, contextualized embeddings can be fine-tuned on specific downstream tasks. This fine-tuning process allows the model to adapt to the specific vocabulary and language patterns of the task at hand, including rare or unseen words. Fine-tuning helps contextualized embeddings become more accurate and robust in handling out-of-vocabulary words.

  Overall, contextualized embeddings have the ability to handle rare or unseen words by leveraging morphological information, transfer learning, subword units, and fine-tuning. While they may not always provide perfect representations for such words, they generally perform better than traditional word embeddings in these cases.