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Pretrained language models have significantly improved the performance of downstream language understanding tasks, including extractive question answering, by providing high-quality contextualized word embeddings.
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Roberta: A robustly optimized bert pretraining approach
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Span selection pre-training for question answering
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Huggingface’s transformers: State-of-the-art natural language processing
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Learning to answer by learning to ask: Getting the best of gpt-2 and bert worlds
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A natural policy gradient
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Training question answering models from synthetic data
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Maximum entropy models for named entity recognition
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Dong Bok Lee, Seanie Lee, Woo Tae Jeong, Donghwan Kim, and Sung Ju Hwang. 2020 · 2005
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Retrofitting structure-aware transformer language model for end tasks
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Tell me how to ask again: Question data augmentation with controllable rewriting in continuous space
Dayiheng Liu, Yeyun Gong, Jie Fu, Yu Yan, Jiusheng Chen, Jiancheng Lv, Nan Duan, and Ming Zhou. 2020 · 2010
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End-to-end synthetic data generation for domain adaptation of question answering systems
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Autoqa: From databases to qa semantic parsers with only synthetic training data
Silei Xu, Sina J Semnani, Giovanni Campagna, and Monica S Lam. 2020 · 2010
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Yikang Shen, Yi Tay, Che Zheng, Dara Bahri, Donald Metzler, and Aaron Courville. 2020 · 2012
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Question answering and question generation as dual tasks
Duyu Tang, Nan Duan, Tao Qin, Zhao Yan, and Ming Zhou. 2017 · 2017
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Attention is all you need
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Bert: Pre-training of deep bidirectional transformers for language understanding
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Emergence of grounded compositional language in multi-agent populations
Igor Mordatch and Pieter Abbeel. 2018 · 2018
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Deep contextualized word representations
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
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Seqgan: Sequence generative adversarial nets with policy gradient
Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu. 2017 · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014 · 2014
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Deterministic policy gradient algorithms
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin Riedmiller. 2014 · 2014
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Nips 2016 tutorial: Generative adversarial networks
Ian Goodfellow. 2016 · 2016
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Multi-agent cooperation and the emergence of (natural) language
Angeliki Lazaridou, Alexander Peysakhovich, and Marco Baroni. 2016 · 2016
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Mutual information and diverse decoding improve neural machine translation
Jiwei Li and Dan Jurafsky. 2016 · 2016
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Self-training for jointly learning to ask and answer questions
Mrinmaya Sachan and Eric Xing. 2018 · 2018
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Straight to the tree: Constituency parsing with neural syntactic distance
Yikang Shen, Zhouhan Lin, Athul Paul Jacob, Alessandro Sordoni, Aaron Courville, and Yoshua Bengio. 2018 · 2018
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Electra: Pre-training text encoders as discriminators rather than generators
Kevin Clark, Minh-Thang Luong, Quoc V Le, and Christopher D Manning. 2019 · 2019
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Mrqa 2019 shared task: Evaluating generalization in reading comprehension
Adam Fisch, Alon Talmor, Robin Jia, Minjoon Seo, Eunsol Choi, and Danqi Chen. 2019 · 2019
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Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al. 2019 · 2019
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Beat the ai: Investigating adversarial human annotation for reading comprehension
Max Bartolo, Alastair Roberts, Johannes Welbl, Sebastian Riedel, and Pontus Stenetorp. 2020 · 2020
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Spanbert: Improving pre-training by representing and predicting spans
Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S Weld, Luke Zettlemoyer, and Omer Levy. 2020 · 2020
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Self-training with noisy student improves imagenet classification
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V Le. 2020 · 2020
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Improving question answering model robustness with synthetic adversarial data generation
Max Bartolo, Tristan Thrush, Robin Jia, Sebastian Riedel, Pontus Stenetorp, and Douwe Kiela. 2021 · 2021
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Question answering infused pre-training of general-purpose contextualized representations
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Paq: 65 million probably-asked questions and what you can do with them
Patrick Lewis, Yuxiang Wu, Linqing Liu, Pasquale Minervini, Heinrich Küttler, Aleksandra Piktus, Pontus Stenetorp, and Sebastian Riedel. 2021 · 2021
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