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--- nlp:question_generation [2022/06/27 20:26] – [Papers] jmflanig
+++ nlp:question_generation [2023/06/15 07:36] (current) – external edit 127.0.0.1
@@ Line 1: / Line 1: @@
 ====== 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 =====
@@ Line 10: / Line 13: @@
   * [[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]]