Bidirectional Encoder Representations from Transformers, or BERT, is a popular language model used in natural language processing (NLP) tasks. It has achieved state-of-the-art results in various NLP applications such as text classification, question-answering, and sentiment analysis. However, like any other machine learning model, BERT has its limitations. In this article, we will discuss some of the limitations of BERT and explore ways to overcome them.
Limitations of BERT
BERT’s contextual understanding is limited
BERT is a contextual language model, meaning it can understand the meaning of a word in its context. However, BERT’s understanding is limited to a fixed-length context, which is typically 512 tokens. This means that BERT may not capture the long-term dependencies between words in a document, resulting in suboptimal performance in tasks that require long-term context understanding.
BERT struggles with rare or unseen words
BERT is a pre-trained model that is trained on a large corpus of text data. However, it may encounter rare or unseen words in a specific domain or application that are not present in its training data. This can result in BERT’s failure to understand the meaning of such words, affecting its overall performance.
BERT has difficulty understanding negation and sarcasm
Negation and sarcasm are challenging concepts for BERT to understand. For example, the sentence “I am not happy” has the opposite meaning of “I am happy,” but BERT may fail to capture this negation due to its contextual understanding limitations. Similarly, BERT may not be able to identify sarcastic remarks, affecting its performance in tasks that require detecting sentiment or emotions in text.
BERT does not consider world knowledge
BERT does not consider world knowledge, meaning it does not have any prior knowledge about the world. For example, if a sentence mentions a specific location, BERT may not understand the significance of that location or its relation to other entities in the sentence. This limitation can affect BERT’s performance in tasks that require a broader understanding of the world.
BERT has a high computational cost
BERT is a large and complex model that requires significant computational resources to train and use. This can be a significant limitation for applications with limited computational resources or in scenarios where real-time processing is required.
BERT is not suitable for certain languages
BERT is trained on a large corpus of English text data and may not perform optimally for languages with different sentence structures or word orders. While
