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Distractor Generation

Given a passage, a question, and an answer phrase, the goal of distractor generation (DG) is to generate context-related wrong options (i.e., distractor) for multiple-choice questions (MCQ).

Papers

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Showing 26–41 of 41 papers

TitleStatusHype
BERT-based distractor generation for Swedish reading comprehension questions using a small-scale datasetCode0
ZmBART: An Unsupervised Cross-lingual Transfer Framework for Language GenerationCode1
Quiz-Style Question Generation for News StoriesCode1
Automatic Distractor Generation for Multiple Choice Questions in Standard Tests—0
A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies.Code1
Better Distractions: Transformer-based Distractor Generation and Multiple Choice Question Filtering—0
A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training StrategiesCode1
Knowledge-Driven Distractor Generation for Cloze-style Multiple Choice Questions—0
Co-Attention Hierarchical Network: Generating Coherent Long Distractors for Reading Comprehension—0
Good, Better, Best: Textual Distractors Generation for Multiple-Choice Visual Question Answering via Reinforcement Learning—0
Equipping Educational Applications with Domain Knowledge—0
Difficulty-aware Distractor Generation for Gap-Fill Items—0
Generating Distractors for Reading Comprehension Questions from Real ExaminationsCode1
Distractor Generation for Multiple Choice Questions Using Learning to RankCode0
Multiple Choice Question Generation Utilizing An Ontology—0
Distractor Generation for Chinese Fill-in-the-blank Items—0
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