Obj2Sub: Unsupervised Conversion of Objective to Subjective Questions

TL;DR

This paper introduces an unsupervised hybrid method that converts objective exam questions into subjective ones, enhancing assessment quality without requiring labeled data.

Contribution

It presents a novel unsupervised approach combining rule-based techniques and pre-trained dense retrievers for question conversion.

Findings

Outperforms existing data-driven methods by 36.45% in Recall@k and Precision@k

Effective for generating subjective questions from objective ones

Advances automated assessment techniques

Abstract

Exams are conducted to test the learner's understanding of the subject. To prevent the learners from guessing or exchanging solutions, the mode of tests administered must have sufficient subjective questions that can gauge whether the learner has understood the concept by mandating a detailed answer. Hence, in this paper, we propose a novel hybrid unsupervised approach leveraging rule-based methods and pre-trained dense retrievers for the novel task of automatically converting the objective questions to subjective questions. We observe that our approach outperforms the existing data-driven approaches by 36.45% as measured by Recall@k and Precision@k.