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Machine reading comprehension (MRC) aims to teach machines to read and comprehend human languages, which is a long-standing goal of natural language processing (NLP).
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Tinybert: Distilling bert for natural language understanding
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Q-bert: Hessian based ultra low precision quantization of bert
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
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LIMIT-BERT: Linguistic informed multi-task bert
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Assessing the benchmarking capacity of machine reading comprehension datasets
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Select, answer and explain: Interpretable multi-hop reading comprehension over multiple documents
Tu, Ming, Kevin Huang, Guangtao Wang, Jing Huang, Xiaodong He, and Bowen Zhou. 2019 · 1911
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Neural module networks for reasoning over text
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Explicit sentence compression for neural machine translation
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Multi-passage machine reading comprehension with cross-passage answer verification
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The argument reasoning comprehension task: Identification and reconstruction of implicit warrants
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Dual processes in reasoning?
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Long short-term memory
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Deep read: A reading comprehension system
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Reading comprehension programs in a statistical-language-processing class
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A rule-based question answering system for reading comprehension tests
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Retrospective reader for machine reading comprehension
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Realm: Retrieval-augmented language model pre-training
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Bleu: a method for automatic evaluation of machine translation
Papineni, Kishore, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers
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In two minds: dual-process accounts of reasoning
Evans, Jonathan St BT. 2003 · 2003
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A framework for evaluation of machine reading comprehension gold standards
Schlegel, Viktor, Marco Valentino, André Freitas, Goran Nenadic, and Riza Batista-Navarro. 2020 · 2003
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From machine reading comprehension to dialogue state tracking: Bridging the gap
Gao, Shuyang, Sanchit Agarwal, Tagyoung Chung, Di Jin, and Dilek Hakkani-Tur. 2020 · 2004
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Multi-choice dialogue-based reading comprehension with knowledge and key turns
Li, Junlong, Zhuosheng Zhang, and Hai Zhao. 2020 · 2004
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Looking for a few good metrics: Automatic summarization evaluation-how many samples are enough?
Lin, Chin-Yew. 2004 · 2004
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Is graph structure necessary for multi-hop reasoning?
Shao, Nan, Yiming Cui, Ting Liu, Shijin Wang, and Guoping Hu. 2020 · 2004
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Mpnet: Masked and permuted pre-training for language understanding
Song, Kaitao, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. 2020 · 2004
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Prerequisites for explainable machine reading comprehension: A position paper
Sugawara, Saku, Pontus Stenetorp, and Akiko Aizawa. 2020 · 2004
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Graph sequential network for reasoning over sequences
Tu, Ming, Jing Huang, Xiaodong He, and Bowen Zhou. 2020 · 2004
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Xu, Canwen, Jiaxin Pei, Hongtao Wu, Yiyu Liu, and Chenliang Li. 2020 · 2004
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Semantics-aware inferential network for natural language understanding
Zhang, Shuailiang, Hai Zhao, and Junru Zhou. 2020 · 2004
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A framework for learning predictive structures from multiple tasks and unlabeled data
Ando, Rie Kubota and Tong Zhang. 2005 · 2005
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Dual multi-head co-attention for multi-choice reading comprehension
Zhu, Pengfei, Hai Zhao, and Xiaoguang Li. 2020 · 2005
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Domain adaptation with structural correspondence learning
Blitzer, John, Ryan McDonald, and Fernando Pereira. 2006 · 2006
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Thinking, fast and slow
Kahneman, Daniel. 2011 · 2011
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Distributed representations of words and phrases and their compositionality
Mikolov, Tomas, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
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Mctest: A challenge dataset for the open-domain machine comprehension of text
Richardson, Matthew, Christopher JC Burges, and Erin Renshaw. 2013 · 2013
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Overview of QA4MRE main task at CLEF 2013
Sutcliffe, Richard, Anselmo Peñas, Eduard Hovy, Pamela Forner, Álvaro Rodrigo, Corina Forascu, Yassine Benajiba, and Petya Osenova. 2013 · 2013
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Neural machine translation by jointly learning to align and translate
Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
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Learning phrase representations using rnn encoder–decoder for statistical machine translation
Cho, Kyunghyun, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
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Glove: Global vectors for word representation
Pennington, Jeffrey, Richard Socher, and Christopher D Manning. 2014 · 2014
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Sequence to sequence learning with neural networks
Sutskever, Ilya, Oriol Vinyals, and Quoc V Le. 2014 · 2014
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Question answering over freebase with multi-column convolutional neural networks
Dong, Li, Furu Wei, Ming Zhou, and Ke Xu. 2015 · 2015
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Teaching machines to read and comprehend
Hermann, Karl Moritz, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
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The goldilocks principle: Reading children’s books with explicit memory representations
Hill, Felix, Antoine Bordes, Sumit Chopra, and Jason Weston. 2015 · 2015
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Pointer networks
Vinyals, Oriol, Meire Fortunato, and Navdeep Jaitly. 2015 · 2015
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Towards AI-complete question answering: a set of prerequisite toy tasks
Song, Linfeng, Zhiguo Wang, Mo Yu, Yue Zhang, Radu Florian, and Daniel Gildea. 2018 · 2018
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What makes reading comprehension questions easier?
Sugawara, Saku, Kentaro Inui, Satoshi Sekine, and Akiko Aizawa. 2018 · 2018
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CliCR: a dataset of clinical case reports for machine reading comprehension
Suster, Simon and Walter Daelemans. 2018 · 2018
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S-net: From answer extraction to answer synthesis for machine reading comprehension
Tan, Chuanqi, Furu Wei, Nan Yang, Bowen Du, Weifeng Lv, and Ming Zhou. 2018 · 2018
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Multi-range reasoning for machine comprehension
Tay, Yi, Luu Anh Tuan, and Siu Cheung Hui. 2018 · 2018
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Weston, Jason, Antoine Bordes, Sumit Chopra, and Tomas Mikolov. 2015 · 2015
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Ms marco: A human generated machine reading comprehension dataset
Bajaj, Payal, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, et al. 2016 · 2016
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Embracing data abundance: Booktest dataset for reading comprehension
Bajgar, Ondrej, Rudolf Kadlec, and Jan Kleindienst. 2016 · 2016
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A thorough examination of the cnn/daily mail reading comprehension task
Chen, Danqi, Jason Bolton, and Christopher D Manning. 2016 · 2016
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WikiReading: A novel large-scale language understanding task over wikipedia
Hewlett, Daniel, Alexandre Lacoste, Llion Jones, Illia Polosukhin, Andrew Fandrianto, Jay Han, Matthew Kelcey, and David Berthelot. 2016 · 2016
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Text understanding with the attention sum reader network
Kadlec, Rudolf, Martin Schmid, Ondrej Bajgar, and Jan Kleindienst. 2016 · 2016
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A corpus and cloze evaluation for deeper understanding of commonsense stories
Mostafazadeh, Nasrin, Nathanael Chambers, Xiaodong He, Devi Parikh, Dhruv Batra, Lucy Vanderwende, Pushmeet Kohli, and James Allen. 2016 · 2016
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Constructing datasets for multi-hop reading comprehension across documents
Welbl, Johannes, Pontus Stenetorp, and Sebastian Riedel. 2018 · 2018
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Large-scale cloze test dataset created by teachers
Xie, Qizhe, Guokun Lai, Zihang Dai, and Eduard Hovy. 2018 · 2018
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DCN+: Mixed objective and deep residual coattention for question answering
Xiong, Caiming, Victor Zhong, and Richard Socher. 2018 · 2018
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Multi-task learning with sample re-weighting for machine reading comprehension
Xu, Yichong, Xiaodong Liu, Yelong Shen, Jingjing Liu, and Jianfeng Gao. 2018 · 2018
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Recipeqa: A challenge dataset for multimodal comprehension of cooking recipes
Yagcioglu, Semih, Aykut Erdem, Erkut Erdem, and Nazli Ikizler-Cinbis. 2018 · 2018
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Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Yang, Zhilin, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D Manning. 2018 · 2018
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QANet: Combining local convolution with global self-attention for reading comprehension
Yu, Adams Wei, David Dohan, Quoc Le, Thang Luong, Rui Zhao, and Kai Chen. 2018 · 2018
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SWAG: A large-scale adversarial dataset for grounded commonsense inference
Zellers, Rowan, Yonatan Bisk, Roy Schwartz, and Yejin Choi. 2018 · 2018
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Subword-augmented embedding for cloze reading comprehension
Zhang, Zhuosheng, Yafang Huang, and Hai Zhao. 2018 · 2018
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One-shot learning for question-answering in gaokao history challenge
Zhang, Zhuosheng and Hai Zhao. 2018 · 2018
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Synthetic qa corpora generation with roundtrip consistency
Alberti, Chris, Daniel Andor, Emily Pitler, Jacob Devlin, and Michael Collins. 2019 · 2019
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Inspecting unification of encoding and matching with transformer: A case study of machine reading comprehension
Bao, Hangbo, Li Dong, Furu Wei, Wenhui Wang, Nan Yang, Lei Cui, Songhao Piao, and Ming Zhou. 2019 · 2019
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Convolutional spatial attention model for reading comprehension with multiple-choice questions
Chen, Zhipeng, Yiming Cui, Wentao Ma, Shijin Wang, and Guoping Hu. 2019 · 2019
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BoolQ: Exploring the surprising difficulty of natural yes/no questions
Clark, Christopher, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. 2019a · 2019
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What does bert look at? an analysis of bert’s attention
Clark, Kevin, Urvashi Khandelwal, Omer Levy, and Christopher D Manning. 2019b · 2019
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Cross-lingual machine reading comprehension
Cui, Yiming, Wanxiang Che, Ting Liu, Bing Qin, Shijin Wang, and Guoping Hu. 2019 · 2019
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Transformer-xl: Attentive language models beyond a fixed-length context
Dai, Zihang, Zhilin Yang, Yiming Yang, Jaime G Carbonell, Quoc Le, and Ruslan Salakhutdinov. 2019 · 2019
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Quoref: A reading comprehension dataset with questions requiring coreferential reasoning
Dasigi, Pradeep, Nelson F. Liu, Ana Marasovic, Noah A. Smith, and Matt Gardner. 2019 · 2019
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Cognitive graph for multi-hop reading comprehension at scale
Ding, Ming, Chang Zhou, Qibin Chen, Hongxia Yang, and Jie Tang. 2019 · 2019
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Unified language model pre-training for natural language understanding and generation
Dong, Li, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2019 · 2019
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Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs
Dua, Dheeru, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner. 2019 · 2019
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Mrqa 2019 shared task: Evaluating generalization in reading comprehension
Fisch, Adam, Alon Talmor, Robin Jia, Minjoon Seo, Eunsol Choi, and Danqi Chen. 2019 · 2019
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A multi-type multi-span network for reading comprehension that requires discrete reasoning
Hu, Minghao, Yuxing Peng, Zhen Huang, and Dongsheng Li. 2019a · 2019
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FlowQA: Grasping flow in history for conversational machine comprehension
Huang, Hsin-Yuan, Eunsol Choi, and Wen tau Yih. 2019 · 2019
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Explore, propose, and assemble: An interpretable model for multi-hop reading comprehension
Jiang, Yichen, Nitish Joshi, Yen-Chun Chen, and Mohit Bansal. 2019 · 2019
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PubMedQA: A dataset for biomedical research question answering
Jin, Qiao, Bhuwan Dhingra, Zhengping Liu, William Cohen, and Xinghua Lu. 2019b · 2019
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Improving neural question generation using answer separation
Kim, Yanghoon, Hwanhee Lee, Joongbo Shin, and Kyomin Jung. 2019 · 2019
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Natural questions: A benchmark for question answering research
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ALBERT: A lite BERT for self-supervised learning of language representations
Lan, Zhenzhong, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2019 · 2019
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Learning with limited data for multilingual reading comprehension
Lee, Kyungjae, Sunghyun Park, Hojae Han, Jinyoung Yeo, Seung-won Hwang, and Juho Lee. 2019 · 2019
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Kagnet: Knowledge-aware graph networks for commonsense reasoning
Lin, Bill Yuchen, Xinyue Chen, Jamin Chen, and Xiang Ren. 2019 · 2019
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Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks
Lu, Jiasen, Dhruv Batra, Devi Parikh, and Stefan Lee. 2019 · 2019
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Multi-style generative reading comprehension
Nishida, Kyosuke, Itsumi Saito, Kosuke Nishida, Kazutoshi Shinoda, Atsushi Otsuka, Hisako Asano, and Junji Tomita. 2019 · 2019
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MCScript2.0: A machine comprehension corpus focused on script events and participants
Ostermann, Simon, Michael Roth, and Manfred Pinkal. 2019 · 2019
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Improving question answering with external knowledge
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Numnet: Machine reading comprehension with numerical reasoning
Ran, Qiu, Yankai Lin, Peng Li, Jie Zhou, and Zhiyuan Liu. 2019b · 2019
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CoQA: A conversational question answering challenge
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Atomic: An atlas of machine commonsense for if-then reasoning
Sap, Maarten, Ronan Le Bras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A Smith, and Yejin Choi. 2019 · 2019
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Multiqa: An empirical investigation of generalization and transfer in reading comprehension
Talmor, Alon and Jonathan Berant. 2019 · 2019
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CommonsenseQA: A question answering challenge targeting commonsense knowledge
Talmor, Alon, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019 · 2019
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Multi-matching network for multiple choice reading comprehension
Tang, Min, Jiaran Cai, and Hankz Hankui Zhuo. 2019 · 2019
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Trick me if you can: Human-in-the-loop generation of adversarial examples for question answering
Wallace, Eric, Pedro Rodriguez, Shi Feng, Ikuya Yamada, and Jordan Boyd-Graber. 2019 · 2019
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Adversarial domain adaptation for machine reading comprehension
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A deep cascade model for multi-document reading comprehension
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HellaSwag: Can a machine really finish your sentence?
Zellers, Rowan, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2019 · 2019
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Examination-style reading comprehension with neural augmented retrieval
Zhang, Yiqing, Hai Zhao, and Zhuosheng Zhang. 2019 · 2019
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Open vocabulary learning for neural chinese pinyin ime
Zhang, Zhuosheng, Yafang Huang, and Hai Zhao. 2019 · 2019
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Learning to ask unanswerable questions for machine reading comprehension
Zhu, Haichao, Li Dong, Furu Wei, Wenhui Wang, Bing Qin, and Ting Liu. 2019 · 2019
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NeurQuRI: Neural question requirement inspector for answerability prediction in machine reading comprehension
Back, Seohyun, Sai Chetan Chinthakindi, Akhil Kedia, Haejun Lee, and Jaegul Choo. 2020 · 2020
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