Masked language model

See also: Machine learning terms

Introduction

In the field of machine learning, particularly natural language processing (NLP), a masked language model (MLM) is an important and widely used approach to train deep learning models on large-scale text data. This unsupervised technique has gained significant attention due to its success in various NLP tasks, such as text classification, translation, and sentiment analysis.

Masked Language Modeling

Masked language modeling is a self-supervised learning technique that aims to predict missing words or tokens in a given text. The primary idea is to train a model on sentences with some words masked (replaced with a special token, usually [MASK]) and have the model predict the correct words in their place. This process allows the model to learn contextual information and gain a deeper understanding of the language structure.

Architecture

The architecture of a masked language model typically consists of a neural network, such as a Transformer, that is designed to process and understand sequential data. The architecture is composed of layers, which include self-attention mechanisms and feed-forward networks, allowing the model to capture both local and global context within the input text.

Training Process

During the training process, a portion of the input tokens is randomly masked. The model is then tasked with predicting the masked tokens based on their surrounding context. The model's predictions are compared to the actual words, and the difference between the predictions and the true words (referred to as the loss) is used to update the model's weights. This process is iterated over a large corpus of text until the model converges to a satisfactory performance.

Applications

Masked language models have been employed in numerous NLP tasks and applications, including:

These models have demonstrated remarkable performance improvements over traditional NLP methods, leading to significant advancements in the field.

Explain Like I'm 5 (ELI5)

Imagine you're reading a book, and some of the words are covered with stickers. You have to guess what those words are based on the words around them. That's what a masked language model does. It learns to understand language by trying to guess the hidden words in sentences. This helps computers get better at things like understanding and writing text.