DisGeM: Distractor Generation for Multiple Choice Questions with Span Masking
Devrim Çavuşoğlu, Seçil Şen, Ulaş Sert · 2024
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.