DisGeM: Distractor Generation for Multiple Choice Questions with Span Masking

• URL: http://arxiv.org/abs/2409.18263v1
• Date: Thu, 26 Sep 2024 20:15:46 GMT
• Title: DisGeM: Distractor Generation for Multiple Choice Questions with Span Masking
• Authors: Devrim Cavusoglu, Secil Sen, Ulas Sert,
• Abstract summary: We present a generic framework for distractor generation for multiple-choice questions (MCQ) Our framework relies solely on pre-trained language models and does not require additional training on specific datasets. Human evaluations confirm that our approach produces more effective and engaging distractors.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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