Masking

TL;DR

• Hide parts of input so the model cannot use irrelevant or sensitive information.
• Focus the model on features relevant to the task.
• Protect sensitive data during training or inference.

Definition

Masking is a technique used in machine learning to hide certain parts of input data. This is often used to prevent the model from seeing certain information that is not relevant to the task at hand, or to protect sensitive information.

Explanation

Masking removes or conceals portions of the input so the model cannot access those elements when making predictions. This limits the model’s view to the information considered relevant for the task and prevents it from relying on irrelevant tokens or sensitive details.

Examples

Sentiment analysis example

Suppose we are training a machine learning model to recognize the sentiment of a given sentence (positive, negative, or neutral). We might want to mask out certain words that are not relevant to the sentiment of the sentence, such as proper nouns or conjunctions. In this case, the model would only see the words that are relevant to the task at hand, and would be unable to use other words to make predictions.

NLP / sensitive-information example

In natural language processing (NLP), masking is often used to protect sensitive information. For instance, suppose we are training a model to extract personal information from a sentence. We might want to mask out certain words, such as names or addresses, to prevent the model from using this sensitive information to make predictions. In this case, the model would only see the words that are relevant to the task at hand, and would be unable to use sensitive information to make predictions.

Use cases

• Preventing models from using information that is not relevant to the task.
• Protecting sensitive information during training or inference.

Related terms

• machine learning
• natural language processing (NLP)
• sentiment analysis
• personal information extraction