Good-Quality Question Generation for Academic Support

The paper presents a metric to automatically compute a score for machine-generated questions and transforms the questions which are having a lower score value, unacceptable, and ungrammatical into a human appealing form. Questions are unacceptable due to the flaws like incorrect grammar, selection of wrong wh-phrase, partial selection of answer phrase, negation, etc. Identifying such infirmities in the question is a challenge. Here, our attempt is to automatically detect and correct the flaws present in the question. We named this system as the Automatic Question Quality Enhancer (AQQE). By employing a multiple linear regression model, AQQE first computes the score (in range of 1–rejected to 5–accepted) for 174 questions. Higher score value tells the acceptance and lower score value shows the rejection of the question. AQQE’s challenge is to enhance the quality of questions having a lower score. Out of 174, human evaluator had identified 84 questions as acceptable and 90 (51.72%) as an unacceptable. Performance of AQQE is judged with precision and recall and it is found well acceptable. AQQE enhanced 79(87.77%) questions are accepted by the human evaluator and 11 (6%) questions can be accepted with further modifications.