Method and apparatus for adversarial training of machine learning model, and medium

An apparatus for adversarial training of a machine learning (ML) model, comprising:

• one or more processors;
• a memory storing instructions executable by the one or more processors;
• wherein the one or more processors are configured to:
• obtain input information in a training sample;
• extract features of a plurality of input characters in the input information;
• input the features of the plurality of input characters to the ML model, to capture an attention weight on an input character of the plurality of input characters by an attention layer of the ML model;
• disturb the attention weight captured by the attention layer, so that the ML model outputs a predicted character according to the attention weight disturbed;
• train the ML model according to a difference between the predicted character and a labeled character in the training sample.

A non-transitory computer-readable storage medium storing computer instructions, wherein when the computer instructions are executed by a computer, the computer is caused to execute a method for adversarial training of a machine learning (ML) model, and the method comprises:

• obtaining input information in a training sample;
• extracting features of a plurality of input characters in the input information;
• inputting the features of the plurality of input characters to the ML model, to capture an attention weight on an input character of the plurality of input characters by an attention layer of the ML model;
• disturbing the attention weight captured by the attention layer, so that the ML model outputs a predicted character according to the attention weight disturbed;
• and
• training the ML model according to a difference between the predicted character and a labeled character in the training sample.