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Automated question generation is an important approach to enable personalisation of English comprehension assessment.
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, J. Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, T. Henighan, R. Child, A. Ramesh, Daniel M. Ziegler, Jeff Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 1901
Earlier work this paper cites.
Question generation by transformers
Kettip Kriangchaivech and Artit Wangperawong. 2019 · 1909
Earlier work this paper cites.
A feasibility study of answer-agnostic question generation for education
Liam Dugan, Eleni Miltsakaki, Shriyash Upadhyay, Etan Ginsberg, Hannah Gonzalez, DaHyeon Choi, Chuning Yuan, and Chris Callison-Burch. 2022 · 1926
Earlier work this paper cites.
A re-examination of text categorization methods
Yiming Yang and Xin Liu. 1999 · 1999
Earlier work this paper cites.
Automatic evaluation of machine translation quality using n-gram co-occurrence statistics
George Doddington. 2002 · 2002
Earlier work this paper cites.
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Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Rouge: A package for automatic evaluation of summaries
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Earlier work this paper cites.
METEOR: An automatic metric for MT evaluation with improved correlation with human judgments
Satanjeev Banerjee and Alon Lavie. 2005 · 2005
Earlier work this paper cites.
Developing reading comprehension questions
R. Day and J.-s Park. 2005 · 2005
Earlier work this paper cites.
Transformer-based end-to-end question generation
Luis Enrico Lopez, Diane Kathryn Cruz, Jan Christian Blaise Cruz, and Charibeth Cheng. 2020 · 2005
Earlier work this paper cites.
HyTER: Meaning-equivalent semantics for translation evaluation
Markus Dreyer and Daniel Marcu. 2012 · 2012
Earlier work this paper cites.
How does english language learning contribute to social mobility of language learners?
Imam Munandar. 2015 · 2015
Earlier work this paper cites.
Truly exploring multiple references for machine translation evaluation
Ying Qin and Lucia Specia. 2015 · 2015
Earlier work this paper cites.
Professor forcing: A new algorithm for training recurrent networks
Alex M Lamb, Anirudh Goyal ALIAS PARTH GOYAL, Ying Zhang, Saizheng Zhang, Aaron C Courville, and Yoshua Bengio. 2016 · 2016
Earlier work this paper cites.
SQuAD: 100,000+ questions for machine comprehension of text
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Nan Duan, Duyu Tang, Peng Chen, and Ming Zhou. 2017 · 2017
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Albert: A lite bert for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2019 · 2019
Later among the works it cites.
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Jingjing Li, Yifan Gao, Lidong Bing, Irwin King, and Michael R Lyu. 2019 · 2019
Later among the works it cites.
A new multi-choice reading comprehension dataset for curriculum learning
Yichan Liang, Jianheng Li, and Jian Yin. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
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Unilmv2: pseudo-masked language models for unified language model pre-training
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Copybert: A unified approach to question generation with self-attention
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A survey on machine reading comprehension—tasks, evaluation metrics and benchmark datasets
Changchang Zeng, Shaobo Li, Qin Li, Jie Hu, and Jianjun Hu. 2020 · 2020
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Guiding the growth: Difficulty-controllable question generation through step-by-step rewriting
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AVA: an automatic eValuation approach for question answering systems
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Answer uncertainty and unanswerability in multiple-choice machine reading comprehension
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