What are Masked Language Models?
Masked Language Models (MLMs) are a class of models used in Natural Language Processing (NLP) to predict missing words in a sentence. Unlike traditional language models that predict the next word in a sequence, MLMs randomly mask certain words in a text, forcing the model to fill in the blanks based on the context provided by the surrounding words. This technique enhances the model's understanding of the language structure and semantics.
The most notable example of a masked language model is BERT (Bidirectional Encoder Representations from Transformers). BERT's architecture allows it to take into account the context from both directions (left and right) of a token, making it highly effective for various NLP tasks such as question answering, sentiment analysis, and named entity recognition.
Training MLMs typically involves two main phases: the masking phase, where a set percentage of input tokens are randomly concealed, and the prediction phase, where the goal is to accurately predict the masked words using the remaining visible context. This process enables MLMs to develop a robust internal representation of language.
In summary, masked language models play a crucial role in advancing Natural Language Processing by improving the understanding of language patterns, which can be applied to numerous AI applications in technology.