MaskedLanguageModeling

Masked language modeling is a training technique widely used in natural language processing for teaching models to understand and generate human language. The core concept involves randomly masking or hiding portions of input text, such as replacing some words with a special token like [MASK], and training the model to predict the original words based on the surrounding context.

This method is considered a form of self-supervised learning because it creates a training objective from the data itself without requiring manually labeled examples. It allows models to learn rich, bidirectional contextual representations of language. Unlike older models that only read text sequentially from left to right, masked language modeling enables a model to consider words on both sides of a masked token, leading to a deeper understanding of linguistic structure, grammar, and semantics.

The most prominent application of this technique is in transformer-based architectures like BERT (Bidirectional Encoder Representations from Transformers). The success of BERT and its variants has established masked language modeling as a foundational pre-training step for many state-of-the-art NLP systems. Models trained this way can then be fine-tuned for various downstream tasks, including text classification, question answering, and named entity recognition.

While highly effective, the method has a limitation. The [MASK] token is only present during training and not during real-world application, which can create a slight mismatch between the training and inference phases. Despite this, masked language modeling remains a cornerstone of modern language model development, enabling machines to achieve a more nuanced comprehension of text.