DisGeM: Distractor Generation for Multiple Choice Questions with Span Masking

Devrim Çavuşoğlu, Seçil Şen, Ulaş Sert

Abstract
Recent advancements in Natural Language Processing (NLP) have impacted numerous sub-fields such as natural language generation, natural language inference, question answering, and more. However, in the field of question generation, the creation of distractors for multiple-choice questions (MCQ) remains a challenging task. In this work, we present a simple, generic framework for distractor generation using readily available Pre-trained Language Models (PLMs). Unlike previous methods, our framework relies solely on pre-trained language models and does not require additional training on specific datasets. Building upon previous research, we introduce a two-stage framework consisting of candidate generation and candidate selection. Our proposed distractor generation framework outperforms previous methods without the need for training or fine-tuning. Human evaluations confirm that our approach produces more effective and engaging distractors. The related codebase is publicly available at https://github.com/obss/disgem.
Anthology ID:
2024.findings-emnlp.568
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2024
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9714–9732
Language:
URL:
https://preview.aclanthology.org/add_missing_videos/2024.findings-emnlp.568/
DOI:
10.18653/v1/2024.findings-emnlp.568
Bibkey:
Cite (ACL):
Devrim Çavuşoğlu, Seçil Şen, and Ulaş Sert. 2024. DisGeM: Distractor Generation for Multiple Choice Questions with Span Masking. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 9714–9732, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
DisGeM: Distractor Generation for Multiple Choice Questions with Span Masking (Çavuşoğlu et al., Findings 2024)
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PDF:
https://preview.aclanthology.org/add_missing_videos/2024.findings-emnlp.568.pdf
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