Reinforcement learning using scaffolding question networks

A method comprising:

• reinforcement learning (RL) teaching of semantic understanding to a content analysis engine of a scaffolding network by:
• receiving a series of questions generated by a question generation engine of a scaffolding network, the series of questions based on a body of content;
• comparing a first content segment of the body of content to a second content segment of the body of the content stored in a session memory to identify a content element present in the first content segment that is not present in the second content segment:
• storing the identified content element in the session memory based on the identification of the content element:
• encoding, based on a previous reward and by a content analysis engine, a question of the series of questions relative to the body of content;
• generating, by the content analysis engine and by processing content elements stored in the session memory, an answer to the question that maximizes a total sum of rewards for answering the series of questions,
• determining a next reward and updating associations between content elements stored in the session memory based on whether the answer is correct or incorrect resulting in updated session memory, and
• answering a next question of the series of questions based on the next reward and based on the updated session memory.

A non-transitory computer readable medium having instructions stored thereon that, when executed by a processing device, cause the processing device to perform operations for reinforcement learning (RL) teaching of semantic understanding to a content analysis engine of a scaffolding network, the operations comprising:

• receiving a series of questions generated by a question generation engine of the scaffolding network, the series of questions based on a body of content;
• comparing a first content segment of the body of content to a second content segment of the body of the content stored in a session memory to identify a content element present in the first content segment that is not present in the second content segment;
• storing the identified content element in the session memory based on the identification of the content element;
• encoding, based on a previous reward and by a content analysis engine, a question of the series of questions relative to the body of content;
• generating, by the content analysis engine and by processing content elements stored in the session memory, an answer to the question that maximizes a total sum of rewards for answering the series of questions;
• determining a next reward and updating associations between content elements stored in the session memory based on whether the answer is correct or incorrect resulting in updated session memory;
• and
• answering a next question of the series of questions based on the next reward and based on the updated session memory.