# Obj2Sub: Unsupervised Conversion of Objective to Subjective Questions

arXiv: https://arxiv.org/abs/2206.11848 · HTML: https://mlsanity.com/paper/2206.11848/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 7 references that are in the map.
- First posted on arXiv 25 May 2022.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Improving Neural Question Generation using Answer Separation](https://mlsanity.com/paper/1809.02393/) (2018)
- [Learning to Generate Questions with Adaptive Copying Neural Networks](https://mlsanity.com/paper/1909.08187/) (2019)

## Abstract (arXiv)

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.