Learning to combine explicit diversity conditions for effective question answer generation

An electronic device comprising:

• at least one processing device configured to:
• predict a question type for each section of a document using a trained question type prediction model, each section including a different portion of the document;
• generate multiple question-answer pairs using a trained question-answer generation model that receives the predicted question types and the document as input, each question-answer pair comprising (i) a question having a type corresponding to one of the predicted question types and being associated with content in the section corresponding to the type and (ii) an answer to the question;
• and
• output the question-answer pairs for use in training a question answering model.

A non-transitory machine-readable medium containing instructions that when executed cause at least one processor of an electronic device to:

• predict a question type for each section of a document using a trained question type prediction model, each section including a different portion of the document;
• generate multiple question-answer pairs using a trained question-answer generation model that receives the predicted question types and the document as input, each question-answer pair comprising (i) a question having a type corresponding to one of the predicted question types and being associated with content in the section corresponding to the type and (ii) an answer to the question;
• and
• output the question-answer pairs for use in training a question answering model.