Question answering information completion using machine reading comprehension-based process

A feedback-type question answering method, comprising:

• receiving, by a data processing system, a question from a user, the data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions which are executed by the at least one processor;
• constructing, by the at least one processor, a training set to detect missing information of the question received by the at least one processor, the question semantically related to a source of information in a document library;
• identifying a plurality of components of the question, the components including information terms of the question;
• generating a plurality of masked questions from the question, including, for each of the masked questions, masking one component of the plurality of components of the question at a time until each of the plurality of components is masked to generate the masked question;
• applying a reading comprehension algorithm to the each masked question to generate new training data;
• generating the new training data from a combination of the source of information, the masked component, the question, and an answer assessment generated from a comparison of the source of information and the component of the question that is masked;
• receiving, by a reinforcement learning (RL) system, the new training data to predict the missing information of the question;
• training, by the at least one processor, a natural language generation model using the missing information, including, in response to the generating the answer assessment, selecting words about which another question is generated for clarifying the question;
• executing, by the at least one processor, the natural language generation model to generate the other question clarifying the question using the selected words;
• combining, by the at least one processor, a response to the other question and the question to generate an input to a language processor;
• generating, by the language processor, a new question;
• applying the new question to a document library;
• and
• generating a final answer.

A computer program product for optimizing a feedback-type question answering process, the computer program product comprising:

• one or more non-transitory computer readable storage media having computer readable program code collectively stored on the one or more computer readable storage media, the computer readable program code being executed by a central processing unit (CPU) of a computer system to cause the computer system to perform a method, said method comprising:
• receiving, by the computer system, a question from a user, the data processing system comprising at least one processor and at least one memory;
• constructing, by the computer system, a training set to detect missing information of the question received by the at least one processor, the question semantically related to a source of information in a document library;
• identifying a plurality of components of the question, the components including information terms of the question;
• generating a plurality of masked questions from the question, including, for each of the masked questions, masking one component of the plurality of components of the question at a time until each of the plurality of components is masked to generate the masked question;
• applying a reading comprehension algorithm to the each masked question to generate new training data;
• generating the new training data from a combination of the source of information, the masked component, the question, and an answer assessment generated from a comparison of the source of information and the component of the question that is masked;
• receiving, by a reinforcement learning (RL) system, the new training data to predict the missing information of the question;
• training, by the computer system, a natural language generation model using the missing information, including selecting words about which another question is generated for clarifying the question, including, in response to the generating the answer assessment, selecting words about which another question is generated for clarifying the question;
• executing, by the computer system, the natural language generation model to generate, including selecting words about which another question is generated for clarifying the question using the selected words;
• combining, by the computer system, a response to the other question and the question to generate an input;
• generating, by the computer system, a new question;
• applying, by the computer system, the new question to a document library;
• and
• generating, by the computer system, a final answer.