用预训练模型生成更有效的多选题干扰项，无需额外训练。
DisGeM: Distractor Generation for Multiple Choice Questions with Span Masking
- 基于掩码跨度的两阶段生成框架，利用现有预训练模型。
- 不需微调，在多个数据集上超越已有方法。
- 人工评估表明干扰项更具迷惑性和吸引力，适合教育应用。
自然语言处理的进展已广泛影响文本生成、自然语言推理、问答等多个领域。然而，在题目生成领域，多选题干扰项的生成仍具挑战性。本文提出一种简单通用的干扰项生成框架，仅依赖现成的预训练语言模型（PLMs），无需针对特定数据集进行额外训练或微调。基于前期研究，我们设计了两阶段流程：候选生成与候选筛选。所提框架在不进行训练的情况下，性能优于以往方法。人工评估证实，该方法生成的干扰项更具有效性和吸引力。相关代码已公开于 https://github.com/obss/disgem。
原文摘要 · Abstract (English)
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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