QDG: A unified model for automatic question-distractor pairs generation

Author:

Shuai Pengju,Li LiORCID,Liu Sishun,Shen Jun

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Reference44 articles.

1. Adamson D, Bhartiya D, Gujral B, Kedia R, Singh A, Rosé CP (2013) Automatically generating discussion questions. In: AIED. Lecture notes in computer science, vol 7926. pp 81–90, Springer

2. Cao ND, Aziz W, Titov I (2019) Question answering by reasoning across documents with graph convolutional networks. In: Association for computational linguistics. NAACL-HLT (1) pp 2306–2317

3. Cao Y, Fang M, Tao D (2019) BAG: Bi-directional attention entity graph convolutional network for multi-hop reasoning question answering. In: NAACL-HLT, (1) pp 357–362, Association for computational linguistics

4. Chen Y, Wu L, Zaki MJ (2020) Reinforcement learning based graph-to-sequence model for natural question generation. In: ICLR. Open Review net

5. Cheng Y, Li S, Liu B, Zhao R, Li S, Lin C, Zheng Y (2021) Guiding the growth: Difficulty-controllable question generation through step-by-step rewriting. In: Association for computational linguistics. ACL/IJCNLP (1) pp 5968–5978

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Deep question generation model based on dual attention guidance;International Journal of Machine Learning and Cybernetics;2024-06-19

2. Clarification question generation diversity and specificity enhancement based on question keyword prediction;Applied Intelligence;2024-02

3. High-quality distractor generation framework for English reading comprehension;2023 5th International Academic Exchange Conference on Science and Technology Innovation (IAECST);2023-12-08

4. Generating assessment tests using image-based items;2023 IEEE International Conference on Data Mining Workshops (ICDMW);2023-12-04

5. Analyzing audiovisual data for understanding user's emotion in human−computer interaction environment;Data Technologies and Applications;2023-11-01

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3