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natural-language-processing-6

Distractor Generation

3260 papers • 126 benchmarks • 313 datasets

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).

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Most implemented papers

Generating Distractors for Reading Comprehension Questions from Real Examinations

Irwin King, Lidong Bing, Piji Li, Michael R. Lyu, Yifan Gao•Fri Sep 07 2018

A hierarchical encoder-decoder framework with static and dynamic attention mechanisms to tackle the task of distractor generation for multiple choice reading comprehension questions from examinations and generates longer and semantic-rich distractors which are closer to distractors in real reading comprehension from examinations.

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Distractor Generation for Multiple Choice Questions Using Learning to Rank

X. Yang, C. Lee Giles, Chen Liang, Neisarg Dave, Drew C. Wham, B. Pursel•Thu May 31 2018

This work investigates how machine learning models, specifically ranking models, can be used to select useful distractors for multiple choice questions and empirically studies feature-based and neural net based ranking models with experiments on the recently released SciQ dataset and the MCQL dataset.

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A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies.

Ho-Lam Chung, Ying-Hong Chan, Yao-Chung Fan•Sun Oct 11 2020

A new distractor generation scheme with multi-tasking and negative answer training strategies for effectively generating multiple distractors that shows strong distracting power for multiple choice question.

48 0

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Quiz-Style Question Generation for News Stories

Cong Yu, Á. Lelkes, Vinh Q. Tran•Wed Feb 17 2021

This work proposes a series of novel techniques for applying large pre-trained Transformer encoder-decoder models, namely PEGASUS and T5, to the tasks of question-answer generation and distractor generation, and shows that these models outperform strong baselines using both automated metrics and human raters.

48 0

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ZmBART: An Unsupervised Cross-lingual Transfer Framework for Language Generation

Kaushal Kumar Maurya, M. Desarkar, Yoshinobu Kano, Kumari Deepshikha•Wed Jun 02 2021

This work proposes an unsupervised cross-lingual language generation framework (called ZmBART) that does not use any parallel or pseudo-parallel/back-translated data and is fine-tuned with task-specific supervised English data and directly evaluated with low-resource languages in the Zero-shot setting.

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BERT-based distractor generation for Swedish reading comprehension questions using a small-scale dataset

Dmytro Kalpakchi, Johan Boye•Sun Aug 08 2021

This paper presents a new BERTbased method for automatically generating distractors using only a small-scale dataset, releases a new such dataset of Swedish MCQs (used for training the model), and proposes a methodology for assessing the generated distractors.

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Distractor Generation | State-of-the-Art