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A summary of must-read papers for Neural Question Generation (NQG)
https://github.com/teacherpeterpan/Question-Generation-Paper-List

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A summary of must-read papers for Neural Question Generation (NQG)

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- Readme: README.md

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awesome-ai-list-guide - Question-Generation-Paper-List - read papers for Neural Question Generation (NQG) (NLP)

README

# Question-Generation-Paper-List
A summary of must-read papers for Neural Question Generation (NQG)

- Contributed by **[Liangming Pan](http://www.liangmingpan.com)**, **[Yuxi Xie](https://yuxixie.github.io/)** and **[Yunxiang Zhang](https://github.com/yunx-z)**

Please follow [this link](./README_by_year.md) to view papers in chronological order.

## [Content](#content)

1. Survey
2. Models

2.1 Basic Seq2Seq Models
2.2 Encoding Answers

2.3 Linguistic Features
2.4 Question-specific Rewards

2.5 Content Selection
2.6 Question Type Modeling

2.7 Encode wider contexts
2.8 QG with pretraining

2.9 Other Directions

2. Applications

2.1 Difficulty Controllable QG
2.2 Conversational QG

2.3 Asking Deep Questions
2.4 Combining QA and QG

2.5 QG from knowledge graphs
2.6 Visual Question Generation

2.7 Distractor Generation
2.8 Cross-lingual QG

2.9 Clarification Question Generation

3. Evaluation
4. Resources

## [Survey papers](#content)
1. **Recent Advances in Neural Question Generation.** arxiv, 2019. [paper](https://arxiv.org/pdf/1905.08949.pdf)

*Liangming Pan, Wenqiang Lei, Tat-Seng Chua, Min-Yen Kan*

2. **A Systematic Review of Automatic Question Generation for Educational Purposes.** International Journal of Artificial Intelligence in Education, 2020. [paper](https://link.springer.com/content/pdf/10.1007/s40593-019-00186-y.pdf)

*Ghader Kurdi, Jared Leo, Bijan Parsia, Uli Sattler, Salam Al-Emari*

3. **A Review on Question Generation from Natural Language Text.** ACM Transactions on Information Systems, Volume 40, Issue 1, 2022. [paper](https://dl.acm.org/doi/pdf/10.1145/3468889)

*Ruqing Zhang, Jiafeng Guo, Lu Chen, Yixing Fan, Xueqi Cheng*

## [Models](#content)

### [Basic Seq2Seq Models](#basic-models)

Basic Seq2Seq models with attention to generate questions.

1. **Learning to ask: Neural question generation for reading comprehension.** ACL, 2017. [paper](https://www.aclweb.org/anthology/P17-1123.pdf)

*Xinya Du, Junru Shao, Claire Cardie.*

2. **Neural question generation from text: A preliminary study.** NLPCC, 2017. [paper](https://www.researchgate.net/profile/Franco_Scarselli/publication/4202380_A_new_model_for_earning_in_raph_domains/links/0c9605188cd580504f000000.pdf)

*Qingyu Zhou, Nan Yang, Furu Wei, Chuanqi Tan, Hangbo Bao, Ming Zhou.*

3. **Machine comprehension by text-to-text neural question generation.** Rep4NLP@ACL, 2017. [paper](https://arxiv.org/pdf/1705.02012.pdf)

*Xingdi Yuan, Tong Wang, Çaglar Gülçehre, Alessandro Sordoni, Philip Bachman, Saizheng Zhang, Sandeep Subramanian, Adam Trischler*

### [Encoding Answers](#answer-encoding)

Applying various techniques to encode the answer information thus allowing for better quality answer-focused questions.

1. **Answer-focused and Position-aware Neural Question Generation.** EMNLP, 2018. [paper](https://www.aclweb.org/anthology/D18-1427)

*Xingwu Sun, Jing Liu, Yajuan Lyu, Wei He, Yanjun Ma, Shi Wang*

2. **Improving Neural Question Generation Using Answer Separation.** AAAI, 2019. [paper](https://arxiv.org/pdf/1809.02393.pdf) [code](https://github.com/yanghoonkim/NQG_ASs2s)

*Yanghoon Kim, Hwanhee Lee, Joongbo Shin, Kyomin Jung.*

3. **Improving Question Generation with Sentence-level Semantic Matching and Answer Position Inferring.** AAAI, 2020. [paper](https://arxiv.org/pdf/1912.00879.pdf)

*Xiyao Ma, Qile Zhu, Yanlin Zhou, Xiaolin Li, Dapeng Wu*

4. **Answer-driven Deep Question Generation based on Reinforcement Learning.** COLING, 2020. [paper](https://www.aclweb.org/anthology/2020.coling-main.452/)

*Liuyin Wang, Zihan Xu, Zibo Lin, Hai-Tao Zheng, Ying Shen*

### [Linguistic Features](#linguistic-features)

Improve QG by incorporating various linguistic features into the QG process.

1. **Neural Generation of Diverse Questions using Answer Focus, Contextual and Linguistic Features.** INLG, 2018. [paper](https://arxiv.org/pdf/1809.02637.pdf)

*Vrindavan Harrison, Marilyn Walker*

2. **Automatic Question Generation using Relative Pronouns and Adverbs.** ACL, 2018. [paper](https://www.aclweb.org/anthology/P18-3022)

*Payal Khullar, Konigari Rachna, Mukul Hase, Manish Shrivastava*

3. **Learning to Generate Questions by Learning What not to Generate.** WWW, 2019. [paper](https://arxiv.org/pdf/1902.10418.pdf) [code](https://github.com/BangLiu/QG)

*Bang Liu, Mingjun Zhao, Di Niu, Kunfeng Lai, Yancheng He, Haojie Wei, Yu Xu.*

4. **Improving Neural Question Generation using World Knowledge.** arXiv, 2019. [paper](https://arxiv.org/pdf/1909.03716.pdf)

*Deepak Gupta, Kaheer Suleman, Mahmoud Adada, Andrew McNamara, Justin Harris*

5. **Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation.** ACL, 2020. [paper](https://arxiv.org/pdf/2004.08694.pdf)

*Kaustubh D. Dhole, Christopher D. Manning*

6. **Automatically Generating Cause-and-Effect Questions from Passages.** EACL Workshop, 2021. [paper](https://www.aclweb.org/anthology/2021.bea-1.17.pdf) [codes](https://github.com/kstats/CausalQG)

*Katherine Stasaski, Manav Rathod, Tony Tu, Yunfang Xiao, Marti A. Hearst*

7. **Asking It All: Generating Contextualized Questions for any Semantic Role.** EMNLP, 2021. [paper](https://arxiv.org/pdf/2109.04832) [codes](https://github.com/ValentinaPy/RoleQGeneration)

*Valentina Pyatkin, Paul Roit, Julian Michael, Yoav Goldberg, Reut Tsarfaty and Ido Dagan*

### [Question-specific Rewards](#RL-rewards)

Improving the training via combining supervised and reinforcement learning to maximize question-specific rewards

1. **Teaching Machines to Ask Questions.** IJCAI, 2018. [paper](https://www.ijcai.org/proceedings/2018/0632.pdf)

*Kaichun Yao, Libo Zhang, Tiejian Luo, Lili Tao, Yanjun Wu*

2. **Natural Question Generation with Reinforcement Learning Based Graph-to-Sequence Model** NeurIPS Workshop, 2019. [paper](https://arxiv.org/pdf/1910.08832.pdf)

*Yu Chen, Lingfei Wu, Mohammed J. Zaki*

3. **Putting the Horse Before the Cart:A Generator-Evaluator Framework for Question Generation from Text** CoNLL, 2019. [paper](https://arxiv.org/pdf/1808.04961.pdf)

*Vishwajeet Kumar, Ganesh Ramakrishnan, Yuan-Fang Li*

4. **Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering** EMNLP, 2019. [paper](https://arxiv.org/pdf/1909.06356.pdf) [code](https://github.com/ZhangShiyue/QGforQA)

*Shiyue Zhang, Mohit Bansal*

5. **Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation** ICLR, 2020. [paper](https://arxiv.org/pdf/1908.04942.pdf) [codes](https://github.com/hugochan/RL-based-Graph2Seq-for-NQG)

*Yu Chen, Lingfei Wu, Mohammed J. Zaki*

12. **Exploring Question-Specific Rewards for Generating Deep Questions.** COLING, 2020. [paper](https://arxiv.org/pdf/2011.01102.pdf) [codes](https://github.com/YuxiXie/RL-for-Question-Generation)

*Yuxi Xie, Liangming Pan, Dongzhe Wang, Min-Yen Kan, Yansong Feng*

13. **Answer-driven Deep Question Generation based on Reinforcement Learning.** COLING, 2020. [paper](https://www.aclweb.org/anthology/2020.coling-main.452/)

*Liuyin Wang, Zihan Xu, Zibo Lin, Hai-Tao Zheng, Ying Shen*

7. **Cooperative Learning of Zero-Shot Machine Reading Comprehension.** arXiv, 2021. [paper](https://arxiv.org/pdf/2103.07449)

*Hongyin Luo, Shang-Wen Li, Seunghak Yu, James Glass*

7. **Contrastive Multi-document Question Generation.** EACL, 2021. [paper](https://www.aclweb.org/anthology/2021.eacl-main.2.pdf) [codes](https://github.com/woonsangcho/contrast_qgen)

*Woon Sang Cho, Yizhe Zhang, Sudha Rao, Asli Celikyilmaz, Chenyan Xiong, Jianfeng Gao, Mengdi Wang, Bill Dolan*

7. **Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning.** EMNLP, 2021. [paper](https://arxiv.org/pdf/2109.04689) [codes](https://github.com/amazon-research/SC2QA-DRIL)

*Li Zhou, Kevin Small, Yong Zhang and Sandeep Atluri*

### [Content Selection](#content-selection)

Improve QG by considering how to select question-worthy contents (content selection) before asking a question.

1. **Identifying Where to Focus in Reading Comprehension for Neural Question Generation.** EMNLP, 2017. [paper](https://www.aclweb.org/anthology/D17-1219.pdf)

*Xinya Du, Claire Cardie*

2. **Neural Models for Key Phrase Extraction and Question Generation.** ACL Workshop, 2018. [paper](https://www.aclweb.org/anthology/W18-2609.pdf)

*Sandeep Subramanian, Tong Wang, Xingdi Yuan, Saizheng Zhang, Adam Trischler, Yoshua Bengio*

3. **A Comparative Study on Question-Worthy Sentence Selection Strategies for Educational Question Generation.** AIED, 2019. [paper](https://link.springer.com/chapter/10.1007/978-3-030-23204-7_6)

*Guanliang Chen, Jie Yang, Dragan Gasevic*

4. **Learning to Generate Questions by Learning What not to Generate.** WWW, 2019. [paper](https://arxiv.org/pdf/1902.10418.pdf) [code](https://github.com/BangLiu/QG)

*Bang Liu, Mingjun Zhao, Di Niu, Kunfeng Lai, Yancheng He, Haojie Wei, Yu Xu.*

5. **Improving Question Generation With to the Point Context.** EMNLP, 2019. [paper](https://arxiv.org/pdf/1910.06036.pdf)

*Jingjing Li, Yifan Gao, Lidong Bing, Irwin King, Michael R. Lyu.*

6. **Weak Supervision Enhanced Generative Network for Question Generation.** IJCAI, 2019. [paper](https://arxiv.org/pdf/1907.00607v1)

*Yutong Wang, Jiyuan Zheng, Qijiong Liu, Zhou Zhao, Jun Xiao, Yueting Zhuang*

7. **A Multi-Agent Communication Framework for Question-Worthy Phrase Extraction and Question Generation.** AAAI, 2019. [paper](https://www.aaai.org/ojs/index.php/AAAI/article/view/4700/4578)

*Siyuan Wang, Zhongyu Wei, Zhihao Fan, Yang Liu, Xuanjing Huang*

8. **Self-Attention Architectures for Answer-Agnostic Neural Question Generation.** ACL, 2019. [paper](https://www.aclweb.org/anthology/P19-1604.pdf)

*Thomas Scialom, Benjamin Piwowarski, Jacopo Staiano.*

9. **Mixture Content Selection for Diverse Sequence Generation.** EMNLP, 2019. [paper](https://arxiv.org/pdf/1909.01953.pdf) [code](https://github.com/clovaai/FocusSeq2Seq)

*Jaemin Cho, Minjoon Seo, Hannaneh Hajishirzi*

10. **Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus.** WWW, 2020. [paper](https://arxiv.org/pdf/2002.00748.pdf)

*Bang Liu, Haojie Wei, Di Niu, Haolan Chen, Yancheng He*

### [Question Type Modeling](#question-type-modeling)

Improve QG by explicitly modeling question types or interrogative words.

1. **Question Generation for Question Answering.** EMNLP,2017. [paper](https://www.aclweb.org/anthology/D17-1090)

*Nan Duan, Duyu Tang, Peng Chen, Ming Zhou*

2. **Answer-focused and Position-aware Neural Question Generation.** EMNLP, 2018. [paper](https://www.aclweb.org/anthology/D18-1427)

*Xingwu Sun, Jing Liu, Yajuan Lyu, Wei He, Yanjun Ma, Shi Wang*

3. **Let Me Know What to Ask: Interrogative-Word-Aware Question Generation** EMNLP Workshop, 2019. [paper](https://arxiv.org/pdf/1910.13794.pdf)

*Junmo Kang, Haritz Puerto San Roman, Sung-Hyon Myaeng*

4. **Question-type Driven Question Generation** EMNLP, 2019. [paper](https://arxiv.org/pdf/1909.00140.pdf)

*Wenjie Zhou, Minghua Zhang, Yunfang Wu*

5. **Expanding, Retrieving and Infilling: Diversifying Cross-Domain Question Generation with Flexible Templates.** EACL, 2021. [paper](https://www.aclweb.org/anthology/2021.eacl-main.279.pdf) [codes](https://github.com/xiaojingyu92/ERIQG)

*Xiaojing Yu, Anxiao Jiang*

### [Encode Wider Contexts](#encode-wider-contexts)

Improve QG by incorporating wider contexts in the input passage.

1. **Harvesting paragraph-level question-answer pairs from wikipedia.** ACL, 2018. [paper](https://arxiv.org/pdf/1805.05942.pdf) [code&dataset](https://github.com/xinyadu/HarvestingQA)

*Xinya Du, Claire Cardie*

2. **Leveraging Context Information for Natural Question Generation** ACL, 2018. [paper](https://www.aclweb.org/anthology/N18-2090) [code](https://github.com/freesunshine0316/MPQG)

*Linfeng Song, Zhiguo Wang, Wael Hamza, Yue Zhang, Daniel Gildea*

3. **Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks.** EMNLP, 2018. [paper](https://www.aclweb.org/anthology/D18-1424.pdf)

*Yao Zhao, Xiaochuan Ni, Yuanyuan Ding, Qifa Ke*

4. **Capturing Greater Context for Question Generation** AAAI, 2020. [paper](https://arxiv.org/pdf/1910.10274.pdf)

*Luu Anh Tuan, Darsh J Shah, Regina Barzilay*

5. **How to Ask Good Questions? Try to Leverage Paraphrases** ACL, 2020. [paper](https://www.aclweb.org/anthology/2020.acl-main.545.pdf)

*Xin Jia, Wenjie Zhou, Xu SUN, Yunfang Wu*

6. **PathQG: Neural Question Generation from Facts** EMNLP, 2020. [paper](http://www.sdspeople.fudan.edu.cn/zywei/paper/2020/wangsy-emnlp-2020.pdf) [code](https://github.com/WangsyGit/PathQG)

*Siyuan Wang, Zhongyu Wei, Zhihao Fan, Zengfeng Huang, Weijian Sun, Qi Zhang, Xuanjing Huang*

7. **AnswerQuest: A System for Generating Question-Answer Items from Multi-Paragraph Documents.** EACL Demo, 2021. [paper](https://arxiv.org/pdf/2103.03820.pdf) [codes](https://github.com/roemmele/answerquest)

*Melissa Roemmele, Deep Sidhpura, Steve DeNeefe, Ling Tsou*

8. **OneStop QAMaker: Extract Question-Answer Pairs from Text in a One-Stop Approach.** arXiv, 2021. [paper](https://arxiv.org/pdf/2102.12128)

*Shaobo Cui, Xintong Bao, Xinxing Zu, Yangyang Guo, Zhongzhou Zhao, Ji Zhang, Haiqing Chen*

9. **ASQ: Automatically Generating Question-Answer Pairs using AMRs.** arXiv, 2021. [paper](https://arxiv.org/pdf/2105.10023)

*Geetanjali Rakshit, Jeffrey Flanigan*

10. **Zero-shot Fact Verification by Claim Generation.** ACL, 2021. [paper](https://arxiv.org/pdf/2105.14682) [codes](https://github.com/teacherpeterpan/Zero-shot-Fact-Verification)

*Liangming Pan, Wenhu Chen, Wenhan Xiong, Min-Yen Kan, William Yang Wang*

11. **Iterative GNN-based Decoder for Question Generation.** EMNLP, 2021. [paper](http://qizhang.info/paper/emnlp2021.3921_Paper.pdf)

*Zichu Fei, Qi Zhang and Yaqian Zhou*

### [QG with pretraining](#qg-with-pretraining)

Improve QG ultilizing NLP pretraining models.

1. **Unified Language Model Pre-training for Natural Language Understanding and Generation.** NeurIPS, 2019. [paper](https://arxiv.org/pdf/1905.03197.pdf) [code](https://github.com/microsoft/unilm)

*Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, Hsiao-Wuen Hon*

2. **A Recurrent BERT-based Model for Question Generation.** MRQA Workshop, 2019. [paper](https://www.aclweb.org/anthology/D19-5821.pdf)

*Ying-Hong Chan, Yao-Chung Fan*

3. **CopyBERT: A Unified Approach to Question Generation with Self-Attention.** ACL Workshop, 2020. [paper](https://www.aclweb.org/anthology/2020.nlp4convai-1.3.pdf) [code](https://github.com/StalVars/CopyBERT)

*Stalin Varanasi, Saadullah Amin, Guenter Neumann*

4. **QURIOUS: Question Generation Pretraining for Text Generation.** arXiv, 2020. [paper](https://arxiv.org/pdf/2004.11026.pdf)

*Shashi Narayan, Gonçalo Simoes, Ji Ma, Hannah Craighead, Ryan Mcdonald*

5. **UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training.** arXiv, 2020. [paper](https://arxiv.org/pdf/2002.12804.pdf) [code](https://github.com/microsoft/unilm/tree/master/unilm)

*Hangbo Bao, Li Dong, Furu Wei, Wenhui Wang, Nan Yang, Xiaodong Liu, Yu Wang, Songhao Piao, Jianfeng Gao, Ming Zhou, Hsiao-Wuen Hon*

### [Other Directions](#other-model)

1. **Generating Question-Answer Hierarchies.** ACL, 2019. [paper](https://arxiv.org/pdf/1906.02622.pdf) [code](http://squash.cs.umass.edu/)

*Kalpesh Krishna and Mohit Iyyer.*

2. **Can You Unpack That? Learning to Rewrite Questions-in-Context.** EMNLP, 2019. [paper](https://www.aclweb.org/anthology/D19-1605.pdf)

*Ahmed Elgohary, Denis Peskov, Jordan L. Boyd-Graber*

3. **Sequential Copying Networks.** AAAI, 2018. [paper](https://arxiv.org/pdf/1807.02301.pdf)

*Qingyu Zhou, Nan Yang, Furu Wei, Ming Zhou*

4. **Let's Ask Again: Refine Network for Automatic Question Generation.** EMNLP, 2019. [paper](https://www.aclweb.org/anthology/D19-1326.pdf)

*Preksha Nema, Akash Kumar Mohankumar, Mitesh M. Khapra, Balaji Vasan Srinivasan, Balaraman Ravindran*

## [Applications](#applications)

### [Difficulty Controllable QG](#difficulty-controllable-QG)

Endowing the model with the ability to control the difficulty of the generated questions.

1. **Easy-to-Hard: Leveraging Simple Questions for Complex Question Generation.** arxiv, 2019. [paper](https://arxiv.org/pdf/1912.02367.pdf)

*Jie Zhao, Xiang Deng, Huan Sun.*

2. **Difficulty Controllable Generation of Reading Comprehension Questions.** IJCAI, 2019. [paper](https://www.ijcai.org/proceedings/2019/0690.pdf)

*Yifan Gao, Lidong Bing, Wang Chen, Michael R. Lyu, Irwin King*

3. **Difficulty-controllable Multi-hop Question Generation From Knowledge Graphs.** ISWC, 2019. [paper](https://arxiv.org/pdf/1807.03586.pdf) [code&dataset](https://github.com/liyuanfang/mhqg)

*Vishwajeet Kumar, Yuncheng Hua, Ganesh Ramakrishnan, Guilin Qi, Lianli Gao, Yuan-Fang Li*

4. **Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting.** ACL, 2021. [paper](https://arxiv.org/pdf/2105.11698) [codes](https://tinyurl.com/19esunzz)

*Yi Cheng, Siyao Li, Bang Liu, Ruihui Zhao, Sujian Li, Chenghua Lin, Yefeng Zheng*

7. **Question Generation for Adaptive Education.** ACL, 2021. [paper](https://arxiv.org/abs/2106.04262) [codes](https://github.com/meghabyte/acl2021-education)

*Megha Srivastava, Noah Goodman*

### [Conversational QG](#conversational-QG)

Learning to generate a series of coherent questions grounded in a question answering style conversation.

1. **Learning to Ask Questions in Open-domain Conversational Systems with Typed Decoders.** ACL, 2018. [paper](https://arxiv.org/pdf/1805.04843.pdf) [code](https://github.com/victorywys/Learning2Ask_TypedDecoder) [dataset]( http://coai.cs.tsinghua.edu.cn/hml/dataset/)

*Yansen Wang, Chenyi Liu, Minlie Huang, Liqiang Nie*

2. **Answerer in Questioner's Mind: Information Theoretic Approach to Goal-Oriented Visual Dialog.** NIPS, 2018. [paper](https://arxiv.org/pdf/1802.03881.pdf)

*Sang-Woo Lee, Yu-Jung Heo, Byoung-Tak Zhang*

3. **Interconnected Question Generation with Coreference Alignment and Conversation Flow Modeling.** ACL, 2019. [paper](https://arxiv.org/pdf/1906.06893.pdf) [code](https://github.com/Evan-Gao/conversational-QG)

*Yifan Gao, Piji Li, Irwin King, Michael R. Lyu*

4. **Reinforced Dynamic Reasoning for Conversational Question Generation.** ACL, 2019. [paper](https://www.aclweb.org/anthology/P19-1203) [code](https://github.com/ZJULearning/ReDR) [dataset](https://stanfordnlp.github.io/coqa/)

*Boyuan Pan, Hao Li, Ziyu Yao, Deng Cai, Huan Sun*

5. **Towards Answer-unaware Conversational Question Generation.** ACL Workshop, 2019. [paper](https://www.aclweb.org/anthology/D19-5809.pdf)

*Mao Nakanishi, Tetsunori Kobayashi, Yoshihiko Hayashi*

6. **What Should I Ask? Using Conversationally Informative Rewards for Goal-oriented Visual Dialog.** ACL, 2019. [paper](https://www.aclweb.org/anthology/P19-1646.pdf)

*Pushkar Shukla, Carlos Elmadjian, Richika Sharan, Vivek Kulkarni, Matthew Turk, William Yang Wang*

7. **Visual Dialogue State Tracking for Question Generation.** AAAI, 2020. [paper](https://arxiv.org/pdf/1911.07928.pdf)

*Wei Pang, Xiaojie Wang*

7. **Interactive Classification by Asking Informative Questions.** ACL, 2020. [paper](https://arxiv.org/pdf/1911.03598.pdf)

*Lili Yu, Howard Chen, Sida Wang, Tao Lei, Yoav Artzi*

7. **Learning to Ask More: Semi-Autoregressive Sequential Question Generation under Dual-Graph Interaction.** ACL, 2020. [paper](https://www.aclweb.org/anthology/2020.acl-main.21.pdf) [dataset](https://github.com/ChaiZ-pku/Sequential-QG)

*Zi Chai, Xiaojun Wan*

8. **Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations.** EMNLP, 2020. [paper](https://arxiv.org/pdf/2004.14530.pdf) [codes](https://github.com/qipeng/stay-hungry-stay-focused)

*Peng Qi, Yuhao Zhang, Christopher D. Manning*

7. **ChainCQG: Flow-Aware Conversational Question Generation.** EACL, 2021. [paper](https://arxiv.org/pdf/2102.02864.pdf) [codes](https://github.com/searchableai/ChainCQG)

*Jing Gu, Mostafa Mirshekari, Zhou Yu, Aaron Sisto*

7. **GTM: A Generative Triple-wise Model for Conversational Question Generation.** ACL, 2021. [paper](https://arxiv.org/abs/2106.03635)

*Lei Shen, Fandong Meng, Jinchao Zhang, Yang Feng, Jie Zhou*

7. **Learning to Ask Conversational Questions by Optimizing Levenshtein Distance.** ACL, 2021. [paper](https://arxiv.org/abs/2106.15903) [codes](https://github.com/LZKSKY/CaSE_RISE)

*Zhongkun Liu, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Maarten de Rijke, Ming Zhou*

### [Asking Deep Questions](#asking-deep-questions)

This direction focuses on exploring how to ask deep questions that require high cognitive levels, such as multi-hop reasoning questions, mathematical questions, open-ended questions, and non-factoid questions.

1. **Automatic Opinion Question Generation.** ICNLG, 2018. [paper](https://www.aclweb.org/anthology/W18-6518.pdf)

*Yllias Chali, Tina Baghaee*

3. **A Multi-language Platform for Generating Algebraic Mathematical Word Problems.** arxiv, 2019. [paper](https://arxiv.org/pdf/1912.01110.pdf)

*Vijini Liyanage, Surangika Ranathunga*

6. **Asking the Crowd: Question Analysis, Evaluation and Generation for Open Discussion on Online Forums.** ACL, 2019. [paper](https://www.aclweb.org/anthology/P19-1497.pdf)

*Zi Chai, Xinyu Xing, Xiaojun Wan, Bo Huang*

7. **Learning to Ask Unanswerable Questions for Machine Reading Comprehension.** ACL, 2019. [paper](https://www.aclweb.org/anthology/P19-1415.pdf)

*Haichao Zhu, Li Dong, Furu Wei, Wenhui Wang, Bing Qin, Ting Liu*

8. **Distant Supervised Why-Question Generation with Passage Self-Matching Attention.** IJCNN, 2019. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8851781)

*Jiaxin Hu, Zhixu Li, Renshou Wu, Hongling Wang, An Liu, Jiajie Xu, Pengpeng Zhao, Lei Zhao*

9. **Conclusion-Supplement Answer Generation for Non-Factoid Questions.** AAAI, 2020. [paper](https://arxiv.org/pdf/1912.00864.pdf)

*Makoto Nakatsuji, Sohei Okui*

9. **Generating Multi-hop Reasoning Questions to Improve Machine Reading Comprehension.** WWW, 2020. [paper](https://dl.acm.org/doi/pdf/10.1145/3366423.3380114)

*Jianxing Yu, Xiaojun Quan, Qinliang Su, Jian Yin*

10. **Low-Resource Generation of Multi-hop Reasoning Questions.** ACL, 2020. [paper](https://www.aclweb.org/anthology/2020.acl-main.601.pdf)

*Jianxing Yu, Wei Liu, Shuang Qiu, Qinliang Su, Kai Wang, Xiaojun Quan, Jian Yin*

11. **Semantic Graphs for Generating Deep Questions.** ACL, 2020. [paper](https://arxiv.org/pdf/2004.12704.pdf) [code](https://github.com/YuxiXie/SG-Deep-Question-Generation)

*Liangming Pan, Yuxi Xie, Yansong Feng, Tat-Seng Chua, Min-Yen Kan*

12. **Review-based Question Generation with Adaptive Instance Transfer and Augmentation.** ACL, 2020. [paper](https://www.aclweb.org/anthology/2020.acl-main.26.pdf)

*Qian Yu, Lidong Bing, Qiong Zhang, Wai Lam, Luo Si*

12. **Inquisitive Question Generation for High Level Text Comprehension.** EMNLP, 2020. [paper](https://arxiv.org/pdf/2010.01657.pdf) [dataset](https://github.com/wjko2/INQUISITIVE)

*Wei-Jen Ko, Te-Yuan Chen, Yiyan Huang, Greg Durrett, Junyi Jessy Li*

12. **Stronger Transformers for Neural Multi-Hop Question Generation.** ArXiv, 2020. [paper](https://arxiv.org/pdf/2010.11374.pdf)

*Devendra Singh Sachan, Lingfei Wu, Mrinmaya Sachan, William Hamilton*

12. **Mathematical Word Problem Generation from Commonsense Knowledge Graph and Equations.** ArXiv, 2020. [paper](https://arxiv.org/pdf/2010.06196.pdf)

*Tianqiao Liu, Qian Fang, Wenbiao Ding, Zhongqin Wu, Zitao Liu*

12. **Reinforced Multi-task Approach for Multi-hop Question Generation.** COLING, 2020. [paper](https://arxiv.org/pdf/2004.02143.pdf)

*Deepak Gupta, Hardik Chauhan, Akella Ravi Tej, Asif Ekbal, Pushpak Bhattacharyya*

12. **Ask to Learn: A Study on Curiosity-driven Question Generation.** COLING, 2020. [paper](https://arxiv.org/pdf/1911.03350.pdf) [codes](https://github.com/YuxiXie/RL-for-Question-Generation)

*Thomas Scialom, Jacopo Staiano*

12. **EQG-RACE: Examination-Type Question Generation.** AAAI, 2021. [paper](https://arxiv.org/pdf/2012.06106.pdf)

*Xin Jia, Wenjie Zhou, Xu Sun, Yunfang Wu*

12. **CliniQG4QA: Generating Diverse Questions for Domain Adaptation of Clinical Question Answering.** NeurIPS Workshop, 2021. [paper](https://arxiv.org/pdf/2010.16021.pdf) [codes](https://github.com/sunlab-osu/CliniQG4QA)

*Xiang Yue, Xinliang Frederick Zhang, Ziyu Yao, Simon Lin, Huan Sun*

7. **Quiz-Style Question Generation for News Stories.** WWW, 2021. [paper](https://arxiv.org/pdf/2102.09094.pdf) [codes](https://github.com/google-research-datasets/NewsQuizQA)

*Adam D. Lelkes, Vinh Q. Tran, Cong Yu*

7. **Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval.** arXiv, 2021. [paper](https://arxiv.org/pdf/2104.08801)

*Devang Kulshreshtha, Robert Belfer, Iulian Vlad Serban, Siva Reddy*

7. **Controllable Open-ended Question Generation with A New Question Type Ontology.** ACL, 2021. [paper](https://arxiv.org/abs/2107.00152) [codes](https://shuyangcao.github.io/projects/ontology_open_ended_question)

*Shuyang Cao, Lu Wang*

### [Combining QA and QG](#Combining-QA-and-QG)

This direction investigate how to combine the task of QA and QG by multi-task learning or joint training.

1. **Question Generation for Question Answering.** EMNLP,2017. [paper](https://www.aclweb.org/anthology/D17-1090)

*Nan Duan, Duyu Tang, Peng Chen, Ming Zhou*

2. **Learning to Collaborate for Question Answering and Asking.** NAACL, 2018. [paper](https://www.aclweb.org/anthology/N18-1141)

*Duyu Tang, Nan Duan, Zhao Yan, Zhirui Zhang, Yibo Sun, Shujie Liu, Yuanhua Lv, Ming Zhou*

3. **Generating Highly Relevant Questions.** EMNLP, 2019. [paper](https://arxiv.org/abs/1910.03401)

*Jiazuo Qiu, Deyi Xiong*

4. **Learning to Answer by Learning to Ask: Getting the Best of GPT-2 and BERT Worlds.** arxiv, 2019. [paper](https://arxiv.org/pdf/1911.02365.pdf)

*Tassilo Klein, Moin Nabi*

5. **Triple-Joint Modeling for Question Generation Using Cross-Task Autoencoder.** NLPCC, 2019. [paper](https://link.springer.com/chapter/10.1007/978-3-030-32236-6_26)

*Hongling Wang, Renshou Wu, Zhixu Li, Zhongqing Wang, Zhigang Chen, Guodong Zhou*

6. **Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering** EMNLP, 2019. [paper](https://arxiv.org/pdf/1909.06356.pdf) [code](https://github.com/ZhangShiyue/QGforQA)

*Shiyue Zhang, Mohit Bansal*

7. **Synthetic QA Corpora Generation with Roundtrip Consistency** ACL, 2019. [paper](https://arxiv.org/pdf/1906.05416.pdf)

*Chris Alberti, Daniel Andor, Emily Pitler, Jacob Devlin, Michael Collins*

7. **Unsupervised Question Answering by Cloze Translation** ACL, 2019. [paper](https://www.aclweb.org/anthology/P19-1484.pdf)

*Patrick Lewis, Ludovic Denoyer, Sebastian Riedel*

9. **Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering.** ACL, 2020. [paper](https://arxiv.org/pdf/2004.11892.pdf)

*Alexander R. Fabbri, Patrick Ng, Zhiguo Wang, Ramesh Nallapati, Bing Xiang*

9. **On the Importance of Diversity in Question Generation for QA.** ACL, 2020. [paper](https://www.aclweb.org/anthology/2020.acl-main.500.pdf)

*Md Arafat Sultan, Shubham Chandel, Ramón Fernandez Astudillo, Vittorio Castelli*

9. **End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems.** EMNLP, 2020. [paper](https://arxiv.org/pdf/2010.06028.pdf)

*Siamak Shakeri, Cicero Nogueira dos Santos, Henry Zhu, Patrick Ng, Feng Nan, Zhiguo Wang, Ramesh Nallapati, Bing Xiang*

9. **Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space.** EMNLP, 2020. [paper](https://arxiv.org/pdf/2010.01475.pdf)

*Dayiheng Liu, Yeyun Gong, Jie Fu, Yu Yan, Jiusheng Chen, Jiancheng Lv, Nan Duan, Ming Zhou*

9. **Training Question Answering Models From Synthetic Data.** EMNLP, 2020. [paper](https://arxiv.org/pdf/2002.09599.pdf)

*Raul Puri, Ryan Spring, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro*

7. **Unsupervised Multi-hop Question Answering by Question Generation.** NAACL, 2021. [paper](https://arxiv.org/pdf/2010.12623.pdf)

*Liangming Pan, Wenhu Chen, Wenhan Xiong, Min-Yen Kan, William Yang Wang*

7. **Data-QuestEval: A Referenceless Metric for Data to Text Semantic Evaluation.** arXiv, 2021. [paper](https://arxiv.org/pdf/2104.07555)

*Clément Rebuffel, Thomas Scialom, Laure Soulier, Benjamin Piwowarski, Sylvain Lamprier, Jacopo Staiano, Geoffrey Scoutheeten, Patrick Gallinari*

7. **Q2: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering** EMNLP, 2021. [paper](https://arxiv.org/pdf/2104.08202)

*Or Honovich, Leshem Choshen, Roee Aharoni, Ella Neeman, Idan Szpektor, Omri Abend*

7. **Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation** arXiv, 2021. [paper](https://arxiv.org/pdf/2104.08678)

*Max Bartolo, Tristan Thrush, Robin Jia, Sebastian Riedel, Pontus Stenetorp, Douwe Kiela*

7. **Cooperative Learning of Zero-Shot Machine Reading Comprehension.** arXiv, 2021. [paper](https://arxiv.org/pdf/2103.07449)

*Hongyin Luo, Shang-Wen Li, Seunghak Yu, James Glass*

7. **Progressively Pretrained Dense Corpus Index for Open-Domain Question Answering.** EACL, 2021. [paper](https://www.aclweb.org/anthology/2021.eacl-main.244.pdf) [codes](https://github.com/xwhan/ProQA.git)

*Wenhan Xiong, Hong Wang, William Yang Wang*

7. **Text Modular Networks: Learning to Decompose Tasks in the Language of Existing Models.** NAACL, 2021. [paper](https://www.aclweb.org/anthology/2021.naacl-main.99.pdf) [codes](https://github.com/allenai/modularqa)

*Tushar Khot, Daniel Khashabi, Kyle Richardson, Peter Clark, Ashish Sabharwal*

7. **Improving Unsupervised Question Answering via Summarization-Informed Question Generation.** EMNLP, 2021. [paper](https://arxiv.org/pdf/2109.07954)

*Chenyang Lyu, Lifeng Shang, Yvette Graham, Jennifer Foster, Xin Jiang, Qun Liu*

### [QG from knowledge graphs](#QG-from-knowledge-graphs)

This direction is about generating questions from a knowledge graph.

1. **Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus.** ACL, 2016. [paper](https://arxiv.org/pdf/1603.06807.pdf) [dataset](https://www.agarciaduran.org)

*Iulian Vlad Serban, Alberto García-Durán, Çaglar Gülçehre, Sungjin Ahn, Sarath Chandar, Aaron C. Courville, Yoshua Bengio*

2. **Generating Natural Language Question-Answer Pairs from a Knowledge Graph Using a RNN Based Question Generation Model.** ACL, 2017. [paper](https://www.aclweb.org/anthology/E17-1036/)

*Mitesh M. Khapra, Dinesh Raghu, Sachindra Joshi, Sathish Reddy*

3. **Knowledge Questions from Knowledge Graphs.** ICTIR, 2017. [paper](https://arxiv.org/pdf/1610.09935.pdf)

*Dominic Seyler, Mohamed Yahya, Klaus Berberich.*

4. **Zero-Shot Question Generation from Knowledge Graphs for Unseen Predicates and Entity Types.** NAACL, 2018. [paper](https://arxiv.org/pdf/1802.06842.pdf) [code](https://github.com/NAACL2018Anonymous/submission)

*Hady Elsahar, Christophe Gravier, Frederique Laforest.*

5. **A Neural Question Generation System Based on Knowledge Base** NLPCC, 2018. [paper](https://link.springer.com/chapter/10.1007/978-3-319-99495-6_12)

*Hao Wang, Xiaodong Zhang, Houfeng Wang*

6. **Formal Query Generation for Question Answering over Knowledge Bases.** ESWC, 2018. [paper](https://link.springer.com/chapter/10.1007/978-3-319-93417-4_46)

*Hamid Zafar, Giulio Napolitano, Jens Lehmann*

7. **Generating Questions for Knowledge Bases via Incorporating Diversified Contexts and Answer-Aware Loss.** EMNLP, 2019. [paper](https://www.aclweb.org/anthology/D19-1247.pdf)

*Cao Liu, Kang Liu, Shizhu He, Zaiqing Nie, Jun Zhao*

8. **Difficulty-controllable Multi-hop Question Generation From Knowledge Graphs.** ISWC, 2019. [paper](https://arxiv.org/pdf/1807.03586.pdf) [code&dataset](https://github.com/liyuanfang/mhqg)

*Vishwajeet Kumar, Yuncheng Hua, Ganesh Ramakrishnan, Guilin Qi, Lianli Gao, Yuan-Fang Li*

9. **How Question Generation Can Help Question Answering over Knowledge Base.** NLPCC, 2019. [paper](http://tcci.ccf.org.cn/conference/2019/papers/183.pdf)

*Sen Hu, Lei Zou, Zhanxing Zhu*

10. **Toward Subgraph Guided Knowledge Graph Question Generation with Graph Neural Networks.** arXiv, 2020. [paper](https://arxiv.org/pdf/2004.06015.pdf)

*Yu Chen, Lingfei Wu, Mohammed J. Zaki*

11. **Generating Semantically Valid Adversarial Questions for TableQA.** arXiv, 2020. [paper](https://arxiv.org/pdf/2005.12696.pdf)

*Yi Zhu, Menglin Xia, Yiwei Zhou*

12. **Knowledge-enriched, Type-constrained and Grammar-guided Question Generation over Knowledge Bases.** COLING, 2020. [paper](https://arxiv.org/pdf/2010.03157.pdf)

*Sheng Bi, Xiya Cheng, Yuan-Fang Li, Yongzhen Wang, Guilin Qi*

### [Visual Question Generation](#visual-question-generation)

Asking questions based on visual inputs (usually an image).

1. **Generating Natural Questions About an Image** ACL, 2016. [paper](https://arxiv.org/pdf/1603.06059.pdf)

*Nasrin Mostafazadeh, Ishan Misra, Jacob Devlin, Margaret Mitchell, Xiaodong He, Lucy Vanderwende*

2. **Creativity: Generating Diverse Questions Using Variational Autoencoders** CVPR,2017. [paper](https://arxiv.org/pdf/1704.03493.pdf)

*Unnat Jain, Ziyu Zhang, Alexander G. Schwing*

3. **Automatic Generation of Grounded Visual Questions** IJCAI, 2017. [paper](https://www.ijcai.org/proceedings/2017/0592.pdf)

*Shijie Zhang, Lizhen Qu, Shaodi You, Zhenglu Yang, Jiawan Zhang*

4. **A Reinforcement Learning Framework for Natural Question Generation using Bi-discriminators** COLING, 2018. [paper](https://www.aclweb.org/anthology/C18-1150.pdf)

*Zhihao Fan, Zhongyu Wei, Siyuan Wang, Yang Liu, Xuanjing Huang*

5. **Customized Image Narrative Generation via Interactive Visual Question Generation and Answering** CVPR, 2018. [paper](https://arxiv.org/pdf/1805.00460.pdf)

*Andrew Shin, Yoshitaka Ushiku, Tatsuya Harada*

6. **Multimodal Differential Network for Visual Question Generation** EMNLP, 2018. [paper](https://www.aclweb.org/anthology/D18-1434.pdf)

*Badri Narayana Patro, Sandeep Kumar, Vinod Kumar Kurmi, Vinay P. Namboodiri*

7. **A Question Type Driven Framework to Diversify Visual Question Generation** IJCAI, 2018. [paper](http://www.sdspeople.fudan.edu.cn/zywei/paper/fan-ijcai2018.pdf)

*Zhihao Fan, Zhongyu Wei, Piji Li, Yanyan Lan, Xuanjing Huang*

8. **Visual Question Generation as Dual Task of Visual Question Answering.** CVPR, 2018. [paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Li_Visual_Question_Generation_CVPR_2018_paper.pdf)

*Yikang Li, Nan Duan, Bolei Zhou, Xiao Chu, Wanli Ouyang, Xiaogang Wang, Ming Zhou*

9. **Two can play this Game: Visual Dialog with Discriminative Question Generation and Answering.** CVPR, 2018. [paper](https://arxiv.org/pdf/1803.11186.pdf)

*Unnat Jain, Svetlana Lazebnik, Alexander Schwing*

10. **Information Maximizing Visual Question Generation.** CVPR, 2019. [paper](https://arxiv.org/pdf/1903.11207.pdf)

*Ranjay Krishna, Michael Bernstein, Li Fei-Fei*

11. **What Should I Ask? Using Conversationally Informative Rewards for Goal-oriented Visual Dialog.** ACL, 2019. [paper](https://www.aclweb.org/anthology/P19-1646.pdf)

*Pushkar Shukla, Carlos Elmadjian, Richika Sharan, Vivek Kulkarni, Matthew Turk, William Yang Wang*

### [Distractor Generation](#distractor-generation)

Learning to generate distractors for multi-choice questions.

1. **Generating Questions and Multiple-Choice Answers using Semantic Analysis of Texts.** COLING, 2016. [paper](https://www.aclweb.org/anthology/C16-1107.pdf)

*Jun Araki, Dheeraj Rajagopal, Sreecharan Sankaranarayanan, Susan Holm, Yukari Yamakawa, Teruko Mitamura*

2. **Distractor Generation for Multiple Choice Questions Using Learning to Rank.** NAACL Workshop, 2018. [paper](https://www.aclweb.org/anthology/W18-0533.pdf) [code](https://github.com/harrylclc/LTR-DG)

*Chen Liang, Xiao Yang, Neisarg Dave, Drew Wham, Bart Pursel, C. Lee Giles*

3. **Generating Distractors for Reading Comprehension Questions from Real Examinations.** AAAI, 2019. [paper](https://arxiv.org/pdf/1809.02768.pdf)

*Yifan Gao, Lidong Bing, Piji Li, Irwin King, Michael R. Lyu*

4. **Knowledge-Driven Distractor Generation for Cloze-style Multiple Choice Questions.** AAAI, 2021. [paper](https://arxiv.org/pdf/2004.09853.pdf)

*Siyu Ren, Kenny Q. Zhu*

### [Cross-lingual QG](#cross-lingual-QG)

Building cross-lingual models to generate questions in low-resource languages.

1. **Cross-Lingual Training for Automatic Question Generation.** ACL, 2019. [paper](https://arxiv.org/pdf/1906.02525.pdf) [dataset](https://www.cse.iitb.ac.in/~ganesh/HiQuAD/clqg/)

*Vishwajeet Kumar, Nitish Joshi, Arijit Mukherjee, Ganesh Ramakrishnan, Preethi Jyothi*

2. **Cross-Lingual Natural Language Generation via Pre-Training.** AAAI, 2020. [paper](https://arxiv.org/pdf/1909.10481.pdf)

*Zewen Chi, Li Dong, Furu Wei, Wenhui Wang, Xian-Ling Mao, Heyan Huang*

7. **Quinductor: a multilingual data-driven method for generating reading-comprehension questions using Universal Dependencies.** arXiv, 2021. [paper](https://arxiv.org/pdf/2103.10121) [codes](https://github.com/dkalpakchi/quinductor)

*Dmytro Kalpakchi, Johan Boye*

### [Clarification Question Generation](#clarification-question-generation)

Learning to ask clarification questions to better understand user intents in conversation, recommendation system, or search engine.

1. **Are You Asking the Right Questions? Teaching Machines to Ask Clarification Questions.** ACL Workshop, 2017. [paper](https://www.aclweb.org/anthology/P17-3006.pdf)

*Sudha Rao*

2. **Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information.** ACL, 2018. [paper](https://arxiv.org/pdf/1805.04655.pdf) [code](https://github.com/raosudha89/ranking_clarification_questions)

*Sudha Rao, Hal Daumé III*

1. **Interpretation of Natural Language Rules in Conversational Machine Reading.** EMNLP, 2018. [paper](https://arxiv.org/pdf/1809.01494.pdf) [dataset](https://sharc-data.github.io/)

*Marzieh Saeidi, Max Bartolo, Patrick Lewis, Sameer Singh, Tim Rocktäschel, Mike Sheldon, Guillaume Bouchard, Sebastian Riedel*

1. **Answer-based Adversarial Training for Generating Clarification Questions.** NAACL, 2019. [paper](https://arxiv.org/pdf/1904.02281.pdf) [code](https://github.com/raosudha89/clarification_question_generation_pytorch)

*Rao S, Daumé III H.*

2. **Asking Clarifying Questions in Open-Domain Information-Seeking Conversations.** SIGIR, 2019. [paper](https://dl.acm.org/doi/pdf/10.1145/3331184.3331265) [dataset](https://github.com/aliannejadi/qulac)

*Mohammad Aliannejadi, Hamed Zamani, Fabio Crestani, W. Bruce Croft*

2. **Asking Clarification Questions in Knowledge-Based Question Answering.** EMNLP, 2019. [paper](https://www.aclweb.org/anthology/D19-1172.pdf) [dataset](https://github.com/msra-nlc/MSParS_V2.0)

*Jingjing Xu, Yuechen Wang, Duyu Tang, Nan Duan, Pengcheng Yang, Qi Zeng, Ming Zhou, Xu Sun*

1. **ClarQ: A large-scale and diverse dataset for Clarification Question Generation.** ACL, 2020. [paper](https://www.aclweb.org/anthology/2020.acl-main.651.pdf) [dataset](https://github.com/vaibhav4595/ClarQ)

*Vaibhav Kumar, Alan W. black.*

2. **Interactive Classification by Asking Informative Questions.** ACL, 2020. [paper](https://arxiv.org/pdf/1911.03598.pdf)

*Lili Yu, Howard Chen, Sida Wang, Tao Lei, Yoav Artzi*

2. **Towards Question-based Recommender Systems.** SIGIR, 2020. [paper](https://arxiv.org/pdf/2005.14255.pdf)

*Jie Zou, Yifan Chen, Evangelos Kanoulas*

2. **Generating Clarifying Questions for Information Retrieval.** WWW, 2020. [paper](http://hamedz.ir/assets/pub/zamani-www2020.pdf)

*Hamed Zamani, Susan T. Dumais, Nick Craswell, Paul N. Bennett, and Gord Lueck*

7. **Diverse and Specific Clarification Question Generation with Keywords** WWW, 2021. [paper](https://arxiv.org/pdf/2104.10317) [codes](https://github.com/blmoistawinde/KPCNet)

*Zhiling Zhang, Kenny Q. Zhu*

7. **Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing** EMNLP, 2021. [paper](https://arxiv.org/pdf/2103.02227)

*Ao Zhang, Kun Wu, Lijie Wang, Zhenghua Li, Xinyan Xiao, Hua Wu, Min Zhang, Haifeng Wang*

7. **Learning to Ask Appropriate Questions in Conversational Recommendation** SIGIR, 2021. [paper](https://arxiv.org/pdf/2105.04774) [codes](https://github.com/XuhuiRen/KBQG)

*Xuhui Ren, Hongzhi Yin, Tong Chen, Hao Wang, Zi Huang, Kai Zheng*

7. **Ask whats missing and whats useful: Improving Clarification Question Generation using Global Knowledge.** NAACL, 2021. [paper](https://www.aclweb.org/anthology/2021.naacl-main.340.pdf) [codes](https://github.com/microsoft/clarification-qgen-globalinfo)

*Bodhisattwa Prasad Majumder, Sudha Rao, Michel Galley, Julian McAuley*

## [Evaluation](#evaluation)

This direction investigates the mechanism behind question asking, and how to evaluate the quality of generated questions.

1. **Question Asking as Program Generation.** NeurIPS, 2017. [paper](https://arxiv.org/pdf/1711.06351.pdf)

*Anselm Rothe, Brenden M. Lake, Todd M. Gureckis.*

2. **Towards a Better Metric for Evaluating Question Generation Systems.** EMNLP, 2018. [paper](https://www.aclweb.org/anthology/D18-1429/)

*Preksha Nema, Mitesh M. Khapra.*

3. **Evaluating Rewards for Question Generation Models.** NAACL, 2019. [paper](https://arxiv.org/pdf/1902.11049.pdf)

*Tom Hosking and Sebastian Riedel.*

## [Resources](#resources)

QG-specific datasets and toolkits.

1. **LearningQ: A Large-Scale Dataset for Educational Question Generation.** ICWSM, 2018. [paper](https://yangjiera.github.io/works/icwsm2018.pdf)

*Guanliang Chen, Jie Yang, Claudia Hauff, Geert-Jan Houben.*

2. **ParaQG: A System for Generating Questions and Answers from Paragraphs.** EMNLP Demo, 2019. [paper](https://arxiv.org/pdf/1909.01642.pdf)

*Vishwajeet Kumar, Sivaanandh Muneeswaran, Ganesh Ramakrishnan, Yuan-Fang Li.*

3. **How to Ask Better Questions? A Large-Scale Multi-Domain Dataset for Rewriting Ill-Formed Questions.** AAAI, 2020. [paper](https://arxiv.org/pdf/1911.09247.pdf) [code](https://github.com/ZeweiChu/MQR)

*Zewei Chu, Mingda Chen, Jing Chen, Miaosen Wang, Kevin Gimpel, Manaal Faruqui, Xiance Si.*

3. **ClarQ: A large-scale and diverse dataset for Clarification Question Generation.** ACL, 2020. [paper](https://www.aclweb.org/anthology/2020.acl-main.651.pdf) [dataset](https://github.com/vaibhav4595/ClarQ)

*Vaibhav Kumar, Alan W. black.*

3. [Toolkit] **Question Generation using transformers** . [github link](https://github.com/patil-suraj/question_generation)

*Suraj Patil*

7. **Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval.** EMNLP, 2021. [paper](https://arxiv.org/pdf/2104.08801)

*Devang Kulshreshtha, Robert Belfer, Iulian Vlad Serban, Siva Reddy*