Contextualized embeddings, such as BERT, have gained popularity in natural language processing tasks due to their ability to capture contextual information. However, there are some limitations to using contextualized embeddings:

  1. Computationally expensive: Contextualized embeddings require significant computational resources. Training large-scale models like BERT can be time-consuming and require high-performance hardware, limiting their accessibility for smaller-scale projects or less powerful machines.

  2. Lack of interpretability: Contextualized embeddings are black-box models, meaning it is difficult to understand how they arrive at their embeddings. This lack of interpretability can be challenging when trying to understand or debug the model's behavior.

  3. Fixed context window: Contextualized embeddings are generated based on a fixed context window size. This means that the embeddings may not be able to capture long-range contextual dependencies effectively. Words outside the defined window may have limited influence on the embeddings, potentially affecting the model's performance.

  4. Lack of domain-specific knowledge: Pretrained contextualized embeddings, such as BERT, are trained on large amounts of text from the internet. While this allows them to capture general language patterns, they may lack domain-specific knowledge. This can be a limitation when working with domain-specific texts or specialized tasks.

  5. Fine-tuning challenges: Even though pretrained contextualized embeddings are effective in transfer learning, fine-tuning them on specific downstream tasks can be challenging. Fine-tuning often requires careful parameter tuning, large amounts of labeled data, and extensive computational resources.

  6. Out-of-vocabulary (OOV) words: Contextualized embeddings generate embeddings based on the context in which words appear during training. This means that words outside the training vocabulary may not have corresponding embeddings, leading to issues with out-of-vocabulary words during inference.

  7. Lack of explicit alignment in multiple languages: Contextualized embeddings are primarily designed for single-language tasks. While there have been efforts to extend them to multilingual tasks, aligning embeddings across multiple languages with different structures and semantics remains a challenge.

  Despite these limitations, contextualized embeddings have proven to be valuable in many natural language processing tasks and continue to be an active area of research in improving their limitations.