Fetching the paper…
Reading the bibliography…
For the field of education, being able to generate semantically correct and educationally relevant multiple choice questions (MCQs) could have a large impact.
Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
Earlier work this paper cites.
Measuring nominal scale agreement among many raters
Joseph L Fleiss. 1971 · 1971
Earlier work this paper cites.
The measurement of observer agreement for categorical data
J Richard Landis and Gary G Koch. 1977 · 1977
Earlier work this paper cites.
Computer-aided generation of multiple-choice tests
Ruslan Mitkov and Le An Ha. 2003 · 2003
Earlier work this paper cites.
Re-evaluating the role of Bleu in machine translation research
Chris Callison-Burch, Miles Osborne, and Philipp Koehn. 2006 · 2006
Earlier work this paper cites.
FAST – an automatic generation system for grammar tests
Chia-Yin Chen, Hsien-Chin Liou, and Jason S. Chang. 2006 · 2006
Earlier work this paper cites.
Automatic factual question generation from text
Michael Heilman. 2011 · 2011
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.
SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
Earlier work this paper cites.
Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
Earlier work this paper cites.
Learning to ask: Neural question generation for reading comprehension
Xinya Du, Junru Shao, and Claire Cardie. 2017 · 2017
Earlier work this paper cites.
RACE: Large-scale ReAding comprehension dataset from examinations
Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy. 2017 · 2017
Earlier work this paper cites.
Universal language model fine-tuning for text classification
Jeremy Howard and Sebastian Ruder. 2018 · 2018
Cited alongside, same era.
Distractor generation for multiple choice questions using learning to rank
Chen Liang, Xiao Yang, Neisarg Dave, Drew Wham, Bart Pursel, and C. Lee Giles. 2018 · 2018
Cited alongside, same era.
Synthetic QA corpora generation with roundtrip consistency
Chris Alberti, Daniel Andor, Emily Pitler, Jacob Devlin, and Michael Collins. 2019 · 2019
Cited alongside, same era.
Contrastive multi-document question generation
Woon Sang Cho, Yizhe Zhang, Sudha Rao, Asli Celikyilmaz, Chenyan Xiong, Jianfeng Gao, Mengdi Wang, and Bill Dolan. 2019 · 2019
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019 · 2019
Later among the works it cites.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Later among the works it cites.
Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019 · 2019
Later among the works it cites.
BERT has a mouth, and it must speak: BERT as a Markov random field language model
Alex Wang and Kyunghyun Cho. 2019 · 2019
Later among the works it cites.
Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, and Jamie Brew. 2019 · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Generating distractors for reading comprehension questions from real examinations
Yifan Gao, Lidong Bing, Piji Li, Irwin King, and Michael R. Lyu. 2019 · 2019
Cited alongside, same era.
Ctrl: A conditional transformer language model for controllable generation
Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, Caiming Xiong, and Richard Socher. 2019 · 2019
Cited alongside, same era.
Improving neural question generation using answer separation
Yanghoon Kim, Hwanhee Lee, Joongbo Shin, and Kyomin Jung. 2019 · 2019
Cited alongside, same era.
Learning to answer by learning to ask: Getting the best of gpt-2 and bert worlds
Tassilo Klein and Moin Nabi. 2019 · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 2019
Cited alongside, same era.
Recent advances in neural question generation
Liangming Pan, Wenqiang Lei, Tat-Seng Chua, and Min-Yen Kan. 2019 · 2019
Cited alongside, same era.
Later among the works it cites.
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey 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 · 2020
Closest in time.
Transformer-based end-to-end question generation
Luis Enrico Lopez, Diane Kathryn Cruz, Jan Christian Blaise Cruz, and Charibeth Cheng. 2020 · 2020
Closest in time.
Knowledge-driven distractor generation for cloze-style multiple choice questions
Siyu Ren and Kenny Q. Zhu. 2020 · 2020
Closest in time.
BLEURT: Learning robust metrics for text generation
Thibault Sellam, Dipanjan Das, and Ankur Parikh. 2020 · 2020
Closest in time.
Retrospective reader for machine reading comprehension
Zhuosheng Zhang, Junjie Yang, and Hai Zhao. 2020 · 2020
Closest in time.
Co-attention hierarchical network: Generating coherent long distractors for reading comprehension
Xiaorui Zhou, Senlin Luo, and Yunfang Wu. 2020 · 2020
Closest in time.
Asking questions the human way: Scalable question-answer generation from text corpus
Bang Liu, Haojie Wei, Di Niu, Haolan Chen, and Yancheng He. 2020 · 2043
Closest in time.