====== Question Generation ======
See [[Question Answering#Synthetic Question Generation|Question Answering - Synthetic Question Generation]].

===== Overviews =====
  * [[https://link.springer.com/content/pdf/10.1007/s40593-019-00186-y.pdf|Kurdi et al 2020 - A Systematic Review of Automatic Question Generation for Educational Purposes]]

===== Papers =====
  * [[https://aclanthology.org/W03-0203.pdf|Mitkov & Ha 2003 - Computer-Aided Generation of Multiple-Choice Tests]]
  * [[http://oro.open.ac.uk/22343/|Boyer & Piwek 2010 - Proceedings of QG2010: The Third Workshop on Question Generation]] [[http://oro.open.ac.uk/22343/1/QG2010-Proceedings.pdf|pdf]]
  * [[https://aclanthology.org/W10-4234.pdf|Rus et al 2010 - The First Question Generation Shared Task Evaluation Challenge]]
  * [[https://aclanthology.org/W11-2853.pdf|Rus et al 2011 - Question Generation Shared Task and Evaluation Challenge – Status Report]]
  * [[https://aclanthology.org/N10-1086.pdf|Heilman & Smith 2011 - Good Question! Statistical Ranking for Question Generation]] (see also Heilman's thesis)
  * [[https://aclanthology.org/W13-2114.pdf|Lindberg et al 2013 - Generating Natural Language Questions to Support Learning On-Line]]
  * [[https://arxiv.org/pdf/1705.00106.pdf|Du et al 2017 - Learning to Ask: Neural Question Generation for Reading Comprehension]]
  * [[https://aclanthology.org/2020.emnlp-main.468.pdf|Puri et al 2020 - Training Question Answering Models From Synthetic Data]] Uses a fine-tuned GPT-2 model to generate synthetic data for SQuAD
  * [[https://link.springer.com/article/10.1007/s40593-019-00186-y|Kurdi et al 2020 - A Systematic Review of Automatic Question Generation for Educational Purposes]] [[https://link.springer.com/content/pdf/10.1007/s40593-019-00186-y.pdf|pdf]]
  * [[https://aclanthology.org/2021.emnlp-main.108.pdf|Pyatkin et al 2021 - Asking It All: Generating Contextualized Questions for any Semantic Role]]
  * [[https://arxiv.org/pdf/2106.08190.pdf|Jia et al 2021 - Question Answering Infused Pre-training of General-Purpose Contextualized Representations]]
  * [[https://arxiv.org/pdf/2209.11000.pdf|Yuan et al 2022 - Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generation]]

==== Generating Unanswerable Questions ====
  * [[https://aclanthology.org/P19-1415.pdf|Zhu et al 2019 - Learning to Ask Unanswerable Questions for Machine Reading Comprehension]]
  * [[https://arxiv.org/pdf/2010.01611.pdf|Nikolenko & Kalehbasti 2020 - When in Doubt, Ask: Generating Answerable and Unanswerable Questions, Unsupervised]]

===== Evaluation =====
  * [[https://arxiv.org/pdf/2204.13921.pdf|Wang et al 2022 - QRelScore: Better Evaluating Generated Questions with Deeper Understanding of Context-aware Relevance]]

===== Datasets and Benchmarks =====
  * **QG-Bench:** [[https://arxiv.org/pdf/2210.03992.pdf|Ushio et al 2022 - Generative Language Models for Paragraph-Level Question Generation]]

===== Resources =====
  * [[https://www.microsoft.com/en-us/research/project/question-generation-qg/|Microsoft - Question Generation]]

===== Related Pages =====
  * [[Question Answering]]
  * [[Paraphrase#Question to Statement]]
  * [[NLP in Education]]