Masked Language Models

What are Masked Language Models?

Masked Language Models (MLMs) are a type of language model used in natural language processing (NLP) tasks. MLMs are trained to predict masked words or tokens in a given input sequence, given the context provided by the surrounding words. This approach is known as the “Cloze task.” MLMs have been popularized by the BERT (Bidirectional Encoder Representations from Transformers) model, which has shown state-of-the-art performance in various NLP tasks.

How do Masked Language Models work?

MLMs are pre-trained on a large corpus of text using unsupervised learning. During training, a certain percentage of the input tokens are randomly masked, and the model learns to predict the masked words based on the context provided by the remaining words. By training on this task, MLMs learn contextual representations of words and can be fine-tuned for various downstream NLP tasks, such as sentiment analysis, named entity recognition, and question-answering.

Some popular MLMs include BERT, RoBERTa, and ALBERT, all of which are based on the Transformer architecture. These models have been widely adopted for various NLP tasks due to their ability to capture bidirectional context and generate meaningful embeddings for words and phrases.

Benefits of Masked Language Models

  • Improved context understanding: MLMs can capture bidirectional context, which helps them better understand the relationships between words in a sentence.

  • Pre-training and fine-tuning: MLMs can be pre-trained on large text corpora, allowing them to learn general language representations. They can then be fine-tuned on specific tasks, resulting in better performance compared to training from scratch.

  • Transfer learning: The pre-trained MLMs can be used as a starting point for various NLP tasks, enabling quick adaptation to new tasks and reducing the need for large labeled datasets.

Resources for learning more about Masked Language Models