%% This BibTeX bibliography file was created using BibDesk.
%% http://bibdesk.sourceforge.net/
%% Saved with string encoding Western (Windows Latin 1) 
@inproceedings{murakhovska-etal-2022-mixqg,
    title = "{M}ix{QG}: Neural Question Generation with Mixed Answer Types",
    author = "Murakhovs{'}ka, Lidiya  and
      Wu, Chien-Sheng  and
      Laban, Philippe  and
      Niu, Tong  and
      Liu, Wenhao  and
      Xiong, Caiming",
    editor = "Carpuat, Marine  and
      de Marneffe, Marie-Catherine  and
      Meza Ruiz, Ivan Vladimir",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.findings-naacl.111",
    doi = "10.18653/v1/2022.findings-naacl.111",
    pages = "1486--1497",
    abstract = "Asking good questions is an essential ability for both human and machine intelligence. However, existing neural question generation approaches mainly focus on short factoid type of answers. In this paper, we introduce a neural question generator, MixQG, to bridge this gap. We combine nine question answering datasets with diverse answer types, including yes/no, multiple-choice, extractive, and abstractive answers, to train a single generative model. We show with empirical results that our model outperforms existing work in both seen and unseen domains, and can generate questions with different cognitive levels when conditioned on different answer types. We run a human evaluation study to assess the quality of generated questions and find that MixQG outperforms the next best model by 10{\%}. Our code and model checkpoints will be released and integrated with the HuggingFace library to facilitate various downstream applications.",
}


@inproceedings{cao2016role,
  title={The role of cause-effect link within scientific paper},
  author={Cao, Mengyun and Sun, Xiaoping and Zhuge, Hai},
  booktitle={2016 12th International Conference on Semantics, Knowledge and Grids (SKG)},
  pages={32--39},
  year={2016},
  organization={IEEE}
}


@inproceedings{stasaski-etal-2021-automatically,
    title = "Automatically Generating Cause-and-Effect Questions from Passages",
    author = "Stasaski, Katherine  and
      Rathod, Manav  and
      Tu, Tony  and
      Xiao, Yunfang  and
      Hearst, Marti A.",
    editor = "Burstein, Jill  and
      Horbach, Andrea  and
      Kochmar, Ekaterina  and
      Laarmann-Quante, Ronja  and
      Leacock, Claudia  and
      Madnani, Nitin  and
      Pil{\'a}n, Ildik{\'o}  and
      Yannakoudakis, Helen  and
      Zesch, Torsten",
    booktitle = "Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.bea-1.17",
    pages = "158--170",
    abstract = "Automated question generation has the potential to greatly aid in education applications, such as online study aids to check understanding of readings. The state-of-the-art in neural question generation has advanced greatly, due in part to the availability of large datasets of question-answer pairs. However, the questions generated are often surface-level and not challenging for a human to answer. To develop more challenging questions, we propose the novel task of cause-and-effect question generation. We build a pipeline that extracts causal relations from passages of input text, and feeds these as input to a state-of-the-art neural question generator. The extractor is based on prior work that classifies causal relations by linguistic category (Cao et al., 2016; Altenberg, 1984). This work results in a new, publicly available collection of cause-and-effect questions. We evaluate via both automatic and manual metrics and find performance improves for both question generation and question answering when we utilize a small auxiliary data source of cause-and-effect questions for fine-tuning. Our approach can be easily applied to generate cause-and-effect questions from other text collections and educational material, allowing for adaptable large-scale generation of cause-and-effect questions.",
}



# emnlp 2023 -> maybe shouldnt be kept as ref (not published yet)
@misc{yoon2023diversity,
      title={Diversity Enhanced Narrative Question Generation for Storybooks}, 
      author={Hokeun Yoon and JinYeong Bak},
      year={2023},
      eprint={2310.16446},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}



@inproceedings{lal-etal-2021-tellmewhy,
    title = "{T}ell{M}e{W}hy: A Dataset for Answering Why-Questions in Narratives",
    author = "Lal, Yash Kumar  and
      Chambers, Nathanael  and
      Mooney, Raymond  and
      Balasubramanian, Niranjan",
    editor = "Zong, Chengqing  and
      Xia, Fei  and
      Li, Wenjie  and
      Navigli, Roberto",
    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-acl.53",
    doi = "10.18653/v1/2021.findings-acl.53",
    pages = "596--610",
}



@inproceedings{ashok-kumar-etal-2023-improving,
    title = "Improving Reading Comprehension Question Generation with Data Augmentation and Overgenerate-and-rank",
    author = "Ashok Kumar, Nischal  and
      Fernandez, Nigel  and
      Wang, Zichao  and
      Lan, Andrew",
    editor = {Kochmar, Ekaterina  and
      Burstein, Jill  and
      Horbach, Andrea  and
      Laarmann-Quante, Ronja  and
      Madnani, Nitin  and
      Tack, Ana{\"\i}s  and
      Yaneva, Victoria  and
      Yuan, Zheng  and
      Zesch, Torsten},
    booktitle = "Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.bea-1.22",
    doi = "10.18653/v1/2023.bea-1.22",
    pages = "247--259",
    abstract = "Reading comprehension is a crucial skill in many aspects of education, including language learning, cognitive development, and fostering early literacy skills in children. Automated answer-aware reading comprehension question generation has significant potential to scale up learner support in educational activities. One key technical challenge in this setting is that there can be multiple questions, sometimes very different from each other, with the same answer; a trained question generation method may not necessarily know which question human educators would prefer. To address this challenge, we propose 1) a data augmentation method that enriches the training dataset with diverse questions given the same context and answer and 2) an overgenerate-and-rank method to select the best question from a pool of candidates. We evaluate our method on the FairytaleQA dataset, showing a 5{\%} absolute improvement in ROUGE-L over the best existing method. We also demonstrate the effectiveness of our method in generating harder, {``}implicit{''} questions, where the answers are not contained in the context as text spans.",
}

@inproceedings{mass-etal-2020-unsupervised,
    title = "Unsupervised {FAQ} Retrieval with Question Generation and {BERT}",
    author = "Mass, Yosi  and
      Carmeli, Boaz  and
      Roitman, Haggai  and
      Konopnicki, David",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.acl-main.74",
    doi = "10.18653/v1/2020.acl-main.74",
    pages = "807--812",
    abstract = "We focus on the task of Frequently Asked Questions (FAQ) retrieval. A given user query can be matched against the questions and/or the answers in the FAQ. We present a fully unsupervised method that exploits the FAQ pairs to train two BERT models. The two models match user queries to FAQ answers and questions, respectively. We alleviate the missing labeled data of the latter by automatically generating high-quality question paraphrases. We show that our model is on par and even outperforms supervised models on existing datasets.",
}


@inproceedings{eo-etal-2023-towards,
    title = "Towards Diverse and Effective Question-Answer Pair Generation from Children Storybooks",
    author = "Eo, Sugyeong  and
      Moon, Hyeonseok  and
      Kim, Jinsung  and
      Hur, Yuna  and
      Kim, Jeongwook  and
      Lee, SongEun  and
      Chun, Changwoo  and
      Park, Sungsoo  and
      Lim, Heuiseok",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.380",
    doi = "10.18653/v1/2023.findings-acl.380",
    pages = "6100--6115",
    abstract = "Recent advances in QA pair generation (QAG) have raised interest in applying this technique to the educational field. However, the diversity of QA types remains a challenge despite its contributions to comprehensive learning and assessment of children. In this paper, we propose a QAG framework that enhances QA type diversity by producing different interrogative sentences and implicit/explicit answers. Our framework comprises a QFS-based answer generator, an iterative QA generator, and a relevancy-aware ranker. The two generators aim to expand the number of candidates while covering various types. The ranker trained on the in-context negative samples clarifies the top-N outputs based on the ranking score. Extensive evaluations and detailed analyses demonstrate that our approach outperforms previous state-of-the-art results by significant margins, achieving improved diversity and quality. Our task-oriented processes are consistent with real-world demand, which highlights our system{'}s high applicability.",
}

@inproceedings{xu2022fairytaleqa,
    author={Xu, Ying and Wang, Dakuo and Yu, Mo and Ritchie, Daniel and Yao, Bingsheng and Wu, Tongshuang and Zhang, Zheng and Li, Toby Jia-Jun and Bradford, Nora and Sun, Branda and Hoang, Tran Bao and Sang, Yisi and Hou, Yufang and Ma, Xiaojuan and Yang, Diyi and Peng, Nanyun and Yu, Zhou and Warschauer, Mark},
    title = {Fantastic Questions and Where to Find Them: Fairytale{QA} -- An Authentic Dataset for Narrative Comprehension},
    publisher = {Association for Computational Linguistics},
    year = {2022}
}
@inproceedings{yao-etal-2022-ais,
    title = "It is {AI}{'}s Turn to Ask Humans a Question: Question-Answer Pair Generation for Children{'}s Story Books",
    author = "Yao, Bingsheng  and
      Wang, Dakuo  and
      Wu, Tongshuang  and
      Zhang, Zheng  and
      Li, Toby  and
      Yu, Mo  and
      Xu, Ying",
    editor = "Muresan, Smaranda  and
      Nakov, Preslav  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-long.54",
    doi = "10.18653/v1/2022.acl-long.54",
    pages = "731--744",
    abstract = "Existing question answering (QA) techniques are created mainly to answer questions asked by humans. But in educational applications, teachers often need to decide what questions they should ask, in order to help students to improve their narrative understanding capabilities. We design an automated question-answer generation (QAG) system for this education scenario: given a story book at the kindergarten to eighth-grade level as input, our system can automatically generate QA pairs that are capable of testing a variety of dimensions of a student{'}s comprehension skills. Our proposed QAG model architecture is demonstrated using a new expert-annotated FairytaleQA dataset, which has 278 child-friendly storybooks with 10,580 QA pairs. Automatic and human evaluations show that our model outperforms state-of-the-art QAG baseline systems. On top of our QAG system, we also start to build an interactive story-telling application for the future real-world deployment in this educational scenario.",
}


@inproceedings{bartolo-etal-2021-improving,
    title = "Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation",
    author = "Bartolo, Max  and
      Thrush, Tristan  and
      Jia, Robin  and
      Riedel, Sebastian  and
      Stenetorp, Pontus  and
      Kiela, Douwe",
    editor = "Moens, Marie-Francine  and
      Huang, Xuanjing  and
      Specia, Lucia  and
      Yih, Scott Wen-tau",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.696",
    doi = "10.18653/v1/2021.emnlp-main.696",
    pages = "8830--8848",
    abstract = "Despite recent progress, state-of-the-art question answering models remain vulnerable to a variety of adversarial attacks. While dynamic adversarial data collection, in which a human annotator tries to write examples that fool a model-in-the-loop, can improve model robustness, this process is expensive which limits the scale of the collected data. In this work, we are the first to use synthetic adversarial data generation to make question answering models more robust to human adversaries. We develop a data generation pipeline that selects source passages, identifies candidate answers, generates questions, then finally filters or re-labels them to improve quality. Using this approach, we amplify a smaller human-written adversarial dataset to a much larger set of synthetic question-answer pairs. By incorporating our synthetic data, we improve the state-of-the-art on the AdversarialQA dataset by 3.7F1 and improve model generalisation on nine of the twelve MRQA datasets. We further conduct a novel human-in-the-loop evaluation and show that our models are considerably more robust to new human-written adversarial examples: crowdworkers can fool our model only 8.8{\%} of the time on average, compared to 17.6{\%} for a model trained without synthetic data.",
}


@inproceedings{sultan-etal-2020-importance,
    title = "On the Importance of Diversity in Question Generation for {QA}",
    author = "Sultan, Md Arafat  and
      Chandel, Shubham  and
      Fernandez Astudillo, Ram{\'o}n  and
      Castelli, Vittorio",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.acl-main.500",
    doi = "10.18653/v1/2020.acl-main.500",
    pages = "5651--5656",
    abstract = "Automatic question generation (QG) has shown promise as a source of synthetic training data for question answering (QA). In this paper we ask: Is textual diversity in QG beneficial for downstream QA? Using top-p nucleus sampling to derive samples from a transformer-based question generator, we show that diversity-promoting QG indeed provides better QA training than likelihood maximization approaches such as beam search. We also show that standard QG evaluation metrics such as BLEU, ROUGE and METEOR are inversely correlated with diversity, and propose a diversity-aware intrinsic measure of overall QG quality that correlates well with extrinsic evaluation on QA.",
}




@inproceedings{rajpurkar-etal-2018-know,
    title = "Know What You Don{'}t Know: Unanswerable Questions for {SQ}u{AD}",
    author = "Rajpurkar, Pranav  and
      Jia, Robin  and
      Liang, Percy",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P18-2124",
    doi = "10.18653/v1/P18-2124",
    pages = "784--789",
}
@inproceedings{rajpurkar-etal-2016-squad,
    title = "{SQ}u{AD}: 100,000+ Questions for Machine Comprehension of Text",
    author = "Rajpurkar, Pranav  and
      Zhang, Jian  and
      Lopyrev, Konstantin  and
      Liang, Percy",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D16-1264",
    doi = "10.18653/v1/D16-1264",
    pages = "2383--2392",
}
@inproceedings{duan-etal-2017-question,
    title = "Question Generation for Question Answering",
    author = "Duan, Nan  and
      Tang, Duyu  and
      Chen, Peng  and
      Zhou, Ming",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D17-1090",
    doi = "10.18653/v1/D17-1090",
    pages = "866--874",
   
}
@inproceedings{DBLP:conf/nips/NguyenRSGTMD16,
  author       = {Tri Nguyen and
                  Mir Rosenberg and
                  Xia Song and
                  Jianfeng Gao and
                  Saurabh Tiwary and
                  Rangan Majumder and
                  Li Deng},
  editor       = {Tarek Richard Besold and
                  Antoine Bordes and
                  Artur S. d'Avila Garcez and
                  Greg Wayne},
  title        = {{MS} {MARCO:} {A} Human Generated MAchine Reading COmprehension Dataset},
  booktitle    = {Proceedings of the Workshop on Cognitive Computation: Integrating
                  neural and symbolic approaches 2016 co-located with the 30th Annual
                  Conference on Neural Information Processing Systems {(NIPS} 2016),
                  Barcelona, Spain, December 9, 2016},
  series       = {{CEUR} Workshop Proceedings},
  volume       = {1773},
  publisher    = {CEUR-WS.org},
  year         = {2016},
  url          = {https://ceur-ws.org/Vol-1773/CoCoNIPS\_2016\_paper9.pdf},
  timestamp    = {Fri, 10 Mar 2023 16:22:16 +0100},
  biburl       = {https://dblp.org/rec/conf/nips/NguyenRSGTMD16.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{kwiatkowski2019natural,
  title={Natural questions: a benchmark for question answering research},
  author={Kwiatkowski, Tom and Palomaki, Jennimaria and Redfield, Olivia and Collins, Michael and Parikh, Ankur and Alberti, Chris and Epstein, Danielle and Polosukhin, Illia and Devlin, Jacob and Lee, Kenton and others},
  journal={Transactions of the Association for Computational Linguistics},
  volume={7},
  pages={453--466},
  year={2019},
  publisher={MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~…}
}
@inproceedings{trischler-etal-2017-newsqa,
    title = "{N}ews{QA}: A Machine Comprehension Dataset",
    author = "Trischler, Adam  and
      Wang, Tong  and
      Yuan, Xingdi  and
      Harris, Justin  and
      Sordoni, Alessandro  and
      Bachman, Philip  and
      Suleman, Kaheer",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W17-2623",
    doi = "10.18653/v1/W17-2623",
    pages = "191--200",
}
@inproceedings{pagnoni-etal-2023-socratic,
    title = "Socratic Pretraining: Question-Driven Pretraining for Controllable Summarization",
    author = "Pagnoni, Artidoro  and
      Fabbri, Alex  and
      Kryscinski, Wojciech  and
      Wu, Chien-Sheng",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.713",
    pages = "12737--12755"
}
@inproceedings{agarwal2011automatic,
  title={Automatic question generation using discourse cues},
  author={Agarwal, Manish and Shah, Rakshit and Mannem, Prashanth},
  booktitle={Proceedings of the Sixth Workshop on Innovative Use of NLP for Building Educational Applications},
  pages={1--9},
  year={2011}
}
@article{dhole2020syn,
  title={Syn-QG: Syntactic and shallow semantic rules for question generation},
  author={Dhole, Kaustubh D and Manning, Christopher D},
  journal={arXiv preprint arXiv:2004.08694},
  year={2020}
}
@inproceedings{heilman-smith-2010-good,
    title = "Good Question! Statistical Ranking for Question Generation",
    author = "Heilman, Michael  and
      Smith, Noah A.",
    booktitle = "Human Language Technologies: The 2010 Annual Conference of the North {A}merican Chapter of the Association for Computational Linguistics",
    month = jun,
    year = "2010",
    address = "Los Angeles, California",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/N10-1086",
    pages = "609--617",
}
@inproceedings{pan-etal-2020-semantic,
    title = "Semantic Graphs for Generating Deep Questions",
    author = "Pan, Liangming  and
      Xie, Yuxi  and
      Feng, Yansong  and
      Chua, Tat-Seng  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.acl-main.135",
    doi = "10.18653/v1/2020.acl-main.135",
    pages = "1463--1475",
  
}
@inproceedings{wang-etal-2020-answer,
    title = "Answer-driven Deep Question Generation based on Reinforcement Learning",
    author = "Wang, Liuyin  and
      Xu, Zihan  and
      Lin, Zibo  and
      Zheng, Haitao  and
      Shen, Ying",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2020.coling-main.452",
    doi = "10.18653/v1/2020.coling-main.452",
    pages = "5159--5170",
   
}
@inproceedings{serban-etal-2016-generating,
    title = "Generating Factoid Questions With Recurrent Neural Networks: The 30{M} Factoid Question-Answer Corpus",
    author = "Serban, Iulian Vlad  and
      Garc{\'\i}a-Dur{\'a}n, Alberto  and
      Gulcehre, Caglar  and
      Ahn, Sungjin  and
      Chandar, Sarath  and
      Courville, Aaron  and
      Bengio, Yoshua",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P16-1056",
    doi = "10.18653/v1/P16-1056",
    pages = "588--598",
}


@inproceedings{kim-etal-2019-improving,
author = {Kim, Yanghoon and Lee, Hwanhee and Shin, Joongbo and Jung, Kyomin},
year = {2019},
month = {07},
pages = {6602-6609},
title = {Improving Neural Question Generation Using Answer Separation},
volume = {33},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
doi = {10.1609/aaai.v33i01.33016602}
}

@inproceedings{li-etal-2017-learning,
title = "Learning through dialogue interactions by asking questions",
abstract = "A good dialogue agent should have the ability to interact with users by both responding to questions and by asking questions, and importantly to learn from both types of interaction. In this work, we explore this direction by designing a simulator and a set of synthetic tasks in the movie domain that allow such interactions between a learner and a teacher. We investigate how a learner can benefit from asking questions in both offline and online reinforcement learning settings, and demonstrate that the learner improves when asking questions. Finally, real experiments with Mechanical Turk validate the approach. Our work represents a first step in developing such end-to-end learned interactive dialogue agents.",
author = "Jiwei Li and Miller, {Alexander H.} and Sumit Chopra and Marc'Aurelio Ranzato and Jason Weston",
note = "Publisher Copyright: {\textcopyright} ICLR 2019 - Conference Track Proceedings. All rights reserved.; 5th International Conference on Learning Representations, ICLR 2017 ; Conference date: 24-04-2017 Through 26-04-2017",
year = "2017",
language = "English (US)",
}

@inproceedings{nakanishi2019towards,
  title={Towards answer-unaware conversational question generation},
  author={Nakanishi, Mao and Kobayashi, Tetsunori and Hayashi, Yoshihiko},
  booktitle={Proceedings of the 2nd Workshop on Machine Reading for Question Answering},
  pages={63--71},
  year={2019}
}

@inproceedings{zamani2020generating,
  title={Generating clarifying questions for information retrieval},
  author={Zamani, Hamed and Dumais, Susan and Craswell, Nick and Bennett, Paul and Lueck, Gord},
  booktitle={Proceedings of the web conference 2020},
  pages={418--428},
  year={2020}
}

@inproceedings{scharpf-etal-2022-collaborative,
  title={Collaborative and AI-aided Exam Question Generation using Wikidata in Education},
  author={Scharpf, Philipp and Schubotz, Moritz and Spitz, Andreas and Greiner-Petter, Andr{\'e} and Gipp, Bela},
  booktitle={Workshop Proceedings},
  pages={18568},
  year={2022}
}


@inproceedings{dugan-etal-2022-feasibility,
    title = "A Feasibility Study of Answer-Agnostic Question Generation for Education",
    author = "Dugan, Liam  and
      Miltsakaki, Eleni  and
      Upadhyay, Shriyash  and
      Ginsberg, Etan  and
      Gonzalez, Hannah  and
      Choi, DaHyeon  and
      Yuan, Chuning  and
      Callison-Burch, Chris",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.findings-acl.151",
    doi = "10.18653/v1/2022.findings-acl.151",
    pages = "1919--1926",
   
}

@inproceedings{rao-etal-2023-generation,
  author={CH, Dhawaleswar Rao and Saha, Sujan Kumar},
  journal={IEEE Transactions on Learning Technologies}, 
  title={Generation of Multiple-Choice Questions From Textbook Contents of School-Level Subjects}, 
  year={2023},
  volume={16},
  number={1},
  pages={40-52},
  doi={10.1109/TLT.2022.3224232}}


@article{DBLP:journals/corr/abs-2203-04464,
  author       = {Jishnu Ray Chowdhury and
                  Debanjan Mahata and
                  Cornelia Caragea},
  title        = {On the Evaluation of Answer-Agnostic Paragraph-level Multi-Question
                  Generation},
  journal      = {CoRR},
  volume       = {abs/2203.04464},
  year         = {2022},
  url          = {https://doi.org/10.48550/arXiv.2203.04464},
  doi          = {10.48550/arXiv.2203.04464},
  eprinttype    = {arXiv},
  eprint       = {2203.04464},
  timestamp    = {Wed, 16 Mar 2022 16:39:52 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2203-04464.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{raffel2020exploring,
  title={Exploring the limits of transfer learning with a unified text-to-text transformer},
  author={Raffel, Colin and Shazeer, Noam and Roberts, Adam and Lee, Katherine and Narang, Sharan and Matena, Michael and Zhou, Yanqi and Li, Wei and Liu, Peter J},
  journal={The Journal of Machine Learning Research},
  volume={21},
  number={1},
  pages={5485--5551},
  year={2020},
  publisher={JMLRORG}
}


@inproceedings{prasad-etal-2008-penn,
    title = "The {P}enn {D}iscourse {T}ree{B}ank 2.0.",
    author = "Prasad, Rashmi  and
      Dinesh, Nikhil  and
      Lee, Alan  and
      Miltsakaki, Eleni  and
      Robaldo, Livio  and
      Joshi, Aravind  and
      Webber, Bonnie",
    booktitle = "Proceedings of the Sixth International Conference on Language Resources and Evaluation ({LREC}'08)",
    month = may,
    year = "2008",
    address = "Marrakech, Morocco",
    publisher = "European Language Resources Association (ELRA)",
}
@article{jiao2022partly,
  title={Partly interpretable transformer through binary arborescent filter for intelligent bearing fault diagnosis},
  author={Jiao, Zhiyuan and Pan, Liren and Fan, Wei and Xu, Zhenying and Chen, Chao},
  journal={Measurement},
  volume={203},
  pages={111950},
  year={2022},
  publisher={Elsevier}
}
@inproceedings{grover2021deep,
  title={Deep learning based question generation using t5 transformer},
  author={Grover, Khushnuma and Kaur, Katinder and Tiwari, Kartikey and Rupali and Kumar, Parteek},
  booktitle={Advanced Computing: 10th International Conference, IACC 2020, Panaji, Goa, India, December 5--6, 2020, Revised Selected Papers, Part I 10},
  pages={243--255},
  year={2021},
  organization={Springer}
}
@article{prasad2019penn,
  title={Penn discourse treebank version 3.0},
  author={Prasad, Rashmi and Webber, Bonnie and Lee, Alan and Joshi, Aravind},
  journal={LDC2019T05},
  year={2019}
}
@article{devlin2018bert,
  title={Bert: Pre-training of deep bidirectional transformers for language understanding},
  author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
  journal={arXiv preprint arXiv:1810.04805},
  year={2018}
}
@inproceedings{nivre-etal-2020-universal,
    title = "{U}niversal {D}ependencies v2: An Evergrowing Multilingual Treebank Collection",
    author = "Nivre, Joakim  and
      de Marneffe, Marie-Catherine  and
      Ginter, Filip  and
      Haji{\v{c}}, Jan  and
      Manning, Christopher D.  and
      Pyysalo, Sampo  and
      Schuster, Sebastian  and
      Tyers, Francis  and
      Zeman, Daniel",
    booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2020.lrec-1.497",
    pages = "4034--4043",
    abstract = "Universal Dependencies is an open community effort to create cross-linguistically consistent treebank annotation for many languages within a dependency-based lexicalist framework. The annotation consists in a linguistically motivated word segmentation; a morphological layer comprising lemmas, universal part-of-speech tags, and standardized morphological features; and a syntactic layer focusing on syntactic relations between predicates, arguments and modifiers. In this paper, we describe version 2 of the universal guidelines (UD v2), discuss the major changes from UD v1 to UD v2, and give an overview of the currently available treebanks for 90 languages.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{fan-etal-2019-eli5,
    title = "{ELI}5: Long Form Question Answering",
    author = "Fan, Angela  and
      Jernite, Yacine  and
      Perez, Ethan  and
      Grangier, David  and
      Weston, Jason  and
      Auli, Michael",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P19-1346",
    doi = "10.18653/v1/P19-1346",
    pages = "3558--3567",
}
@article{marcus-etal-1993-building,
    title = "Building a Large Annotated Corpus of {E}nglish: The {P}enn {T}reebank",
    author = "Marcus, Mitchell P.  and
      Santorini, Beatrice  and
      Marcinkiewicz, Mary Ann",
    journal = "Computational Linguistics",
    volume = "19",
    number = "2",
    year = "1993",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://aclanthology.org/J93-2004",
    pages = "313--330",
}
@article{alberti2019synthetic,
  title={Synthetic QA corpora generation with roundtrip consistency},
  author={Alberti, Chris and Andor, Daniel and Pitler, Emily and Devlin, Jacob and Collins, Michael},
  journal={arXiv preprint arXiv:1906.05416},
  year={2019}
}
@article{liu2020tell,
  title={Tell me how to ask again: Question data augmentation with controllable rewriting in continuous space},
  author={Liu, Dayiheng and Gong, Yeyun and Fu, Jie and Yan, Yu and Chen, Jiusheng and Lv, Jiancheng and Duan, Nan and Zhou, Ming},
  journal={arXiv preprint arXiv:2010.01475},
  year={2020}
}
@inproceedings{liu2020neural,
  title={Neural question generation based on Seq2Seq},
  author={Liu, Bingran},
  booktitle={Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence},
  pages={119--123},
  year={2020}
}
@inproceedings{fei-etal-2022-cqg,
    title = "{CQG}: A Simple and Effective Controlled Generation Framework for Multi-hop Question Generation",
    author = "Fei, Zichu  and
      Zhang, Qi  and
      Gui, Tao  and
      Liang, Di  and
      Wang, Sirui  and
      Wu, Wei  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-long.475",
    doi = "10.18653/v1/2022.acl-long.475",
    pages = "6896--6906",
}
@inproceedings{yang-etal-2018-hotpotqa,
    title = "{H}otpot{QA}: A Dataset for Diverse, Explainable Multi-hop Question Answering",
    author = "Yang, Zhilin  and
      Qi, Peng  and
      Zhang, Saizheng  and
      Bengio, Yoshua  and
      Cohen, William  and
      Salakhutdinov, Ruslan  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D18-1259",
    doi = "10.18653/v1/D18-1259",
    pages = "2369--2380",
}
@inproceedings{xue-etal-2016-conll,
    title = "{C}o{NLL} 2016 Shared Task on Multilingual Shallow Discourse Parsing",
    author = "Xue, Nianwen  and
      Ng, Hwee Tou  and
      Pradhan, Sameer  and
      Rutherford, Attapol  and
      Webber, Bonnie  and
      Wang, Chuan  and
      Wang, Hongmin",
    booktitle = "Proceedings of the {C}o{NLL}-16 shared task",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/K16-2001",
    doi = "10.18653/v1/K16-2001",
    pages = "1--19",
}
@inproceedings{prasad2008discourse,
  title={A discourse-based approach to generating why-questions from texts},
  author={Prasad, Rashmi and Joshi, Aravind},
  booktitle={Proceedings of the Workshop on the Question Generation Shared Task and Evaluation Challenge},
  pages={1--3},
  year={2008},
  organization={National Science Foundation Arlington, VA}
}
@inproceedings{scialom-etal-2019-self,
    title = "Self-Attention Architectures for Answer-Agnostic Neural Question Generation",
    author = "Scialom, Thomas  and
      Piwowarski, Benjamin  and
      Staiano, Jacopo",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P19-1604",
    doi = "10.18653/v1/P19-1604",
    pages = "6027--6032",
   
}
@inproceedings{papineni-etal-2002-bleu,
    title = "{B}leu: a Method for Automatic Evaluation of Machine Translation",
    author = "Papineni, Kishore  and
      Roukos, Salim  and
      Ward, Todd  and
      Zhu, Wei-Jing",
    booktitle = "Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2002",
    address = "Philadelphia, Pennsylvania, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P02-1040",
    doi = "10.3115/1073083.1073135",
    pages = "311--318",
}
@inproceedings{lin-2004-rouge,
    title = "{ROUGE}: A Package for Automatic Evaluation of Summaries",
    author = "Lin, Chin-Yew",
    booktitle = "Text Summarization Branches Out",
    month = jul,
    year = "2004",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W04-1013",
    pages = "74--81",
}
@article{lavie2009meteor,
  title={The METEOR metric for automatic evaluation of machine translation},
  author={Lavie, Alon and Denkowski, Michael J},
  journal={Machine translation},
  volume={23},
  pages={105--115},
  year={2009},
  publisher={Springer}
}
 @inproceedings{devlin-etal-2019-bert,
    title = "{BERT}: Pre-training of Deep Bidirectional Transformers for Language Understanding",
    author = "Devlin, Jacob  and
      Chang, Ming-Wei  and
      Lee, Kenton  and
      Toutanova, Kristina",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/N19-1423",
    doi = "10.18653/v1/N19-1423",
    pages = "4171--4186",
    
}
@article{lopez-2020-transformer,
  title={Transformer-based End-to-End Question Generation},
  author={Lopez, Luis Enrico and Cruz, Diane Kathryn and Cruz, Jan Christian Blaise and Cheng, Charibeth},
   journal={arXiv preprint arXiv:2005.01107},
  year={2020},
volume={4}
}



@inproceedings{alberti-etal-2019-synthetic,
    title = "Synthetic {QA} Corpora Generation with Roundtrip Consistency",
    author = "Alberti, Chris  and
      Andor, Daniel  and
      Pitler, Emily  and
      Devlin, Jacob  and
      Collins, Michael",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P19-1620",
    doi = "10.18653/v1/P19-1620",
    pages = "6168--6173",
    
}
@inproceedings{liu-etal-2020-tell,
    title = "Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space",
    author = "Liu, Dayiheng  and
      Gong, Yeyun  and
      Fu, Jie  and
      Yan, Yu  and
      Chen, Jiusheng  and
      Lv, Jiancheng  and
      Duan, Nan  and
      Zhou, Ming",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.emnlp-main.467",
    doi = "10.18653/v1/2020.emnlp-main.467",
    pages = "5798--5810",
    
}
@inproceedings{DBLP:conf/iclr/LiMCRW17a,
  author       = {Jiwei Li and
                  Alexander H. Miller and
                  Sumit Chopra and
                  Marc'Aurelio Ranzato and
                  Jason Weston},
  title        = {Learning through Dialogue Interactions by Asking Questions},
  booktitle    = {5th International Conference on Learning Representations, {ICLR} 2017,
                  Toulon, France, April 24-26, 2017, Conference Track Proceedings},
  publisher    = {OpenReview.net},
  year         = {2017},
  url          = {https://openreview.net/forum?id=rkE8pVcle},
  timestamp    = {Thu, 25 Jul 2019 14:26:00 +0200},
  biburl       = {https://dblp.org/rec/conf/iclr/LiMCRW17a.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}





