Question-and-answer system and method of controlling the same

A method of controlling a question-and-answer system, the method comprising:

• storing a plurality of representative questions to match a plurality of answers corresponding respectively to the plurality of representative questions;
• performing multi task learning by using a plurality of extended sentences for the plurality of representative questions as input data, and by using the plurality of representative questions corresponding respectively to the plurality of extended sentences, and a plurality of categories to which the plurality of extended sentences belong, respectively, as output data;
• in response to the multi task learning being completed, determining a representative question corresponding to an input sentence uttered by a user from among the stored plurality of representative questions based on a result of the multi-task learning;
• and
• determining an answer that matches the determined representative question from among the stored plurality of answers;
• wherein performing the multi task learning includes performing the multi-task learning by using the plurality of extended sentences as input data, and by using the plurality of representative questions, the plurality of categories, and a plurality of named entities included in the plurality of extended sentences, respectively, as output data;
• wherein performing the multi task learning further includes classifying a representative question corresponding to the input data from among the stored plurality of representative questions, a category to which the input data belongs from among the plurality of categories, and a named entity included in the input data from among the plurality of named entities, and
• wherein performing the multi task learning additionally includes calculating a loss value of the classified representative question, a loss value of the classified category, and a loss value of the classified named entity, and adjusting a weight of a deep learning model used for the multi-tasking learning based on the three calculated loss values.