Adversarial training of machine learning models

A method performed on a computing device, the method comprising:

• accessing training samples for a machine learning model having two or more layers and parameters;
• training the machine learning model by:
• performing noise adjustment by adding noise to embeddings produced by the machine learning model that represent text of the training samples to obtain noise-adjusted embeddings, and
• adjusting the parameters of the machine learning model to obtain a trained machine learning model having adjusted parameters, the adjusting being based at least on a difference between first output distributions determined by the machine learning model using the embeddings representing the text of the training samples and second output distributions determined by the machine learning model using the noise-adjusted embeddings, wherein the first output distributions and the second output distributions are output by the machine learning model during training, and wherein the first output distributions and the second output distributions include different predicted values for a particular training sample;
• and
• outputting the trained machine learning model having the adjusted parameters.

A system comprising:

• a hardware processing unit;
• and
• a storage resource storing computer-readable instructions which, when executed by the hardware processing unit, cause the hardware processing unit to:
• receive input data;
• process the input data using a machine learning model having two or more layers that have been trained together to obtain a result, the two or more layers having been trained based at least on a difference between first output distributions determined by the machine learning model using embeddings representing text of training samples and second output distributions determined by the machine learning model using noise-adjusted embeddings obtained by adding noise to the embeddings, wherein the first output distributions and the second output distributions are output by the machine learning model during training and include different predicted values for a particular training sample;
• and
• output the result.

A method performed on a computing device, the method comprising:

• receiving input data;
• processing the input data using a machine learning model having two or more layers that have been trained together to obtain a result, the two or more layers having been trained based at least on a difference between first output distributions determined by the machine learning model using embeddings representing text of training samples and second output distributions determined by the machine learning model using noise-adjusted embeddings obtained by adding noise to the embeddings, wherein the first output distributions and the second output distributions are output by the machine learning model during training and include different predicted values for a particular training sample;
• and
• outputting the result.