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Multi-choice Machine Reading Comprehension (MRC) requires model to decide the correct answer from a set of answer options when given a passage and a question.
Option Comparison Network for Multiple-choice Reading Comprehension
Ran, Q.; Li, P.; Hu, W.; and Zhou, J. 2019 · 1903
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XLNet: Generalized Autoregressive Pretraining for Language Understanding
Yang, Z.; Dai, Z.; Yang, Y.; Carbonell, J.; Salakhutdinov, R.; and Le, Q. V. 2019 · 1906
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RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y.; Ott, M.; Goyal, N.; Du, J.; Joshi, M.; Chen, D.; Levy, O.; Lewis, M.; Zettlemoyer, L.; and Stoyanov, V. 2019 · 1907
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Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
Shoeybi, M.; Patwary, M.; Puri, R.; LeGresley, P.; Casper, J.; and Catanzaro, B. 2019 · 1909
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Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C.; Shazeer, N.; Roberts, A.; Lee, K.; Narang, S.; Matena, M.; Zhou, Y.; Li, W.; and Liu, P. J. 2019 · 1910
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Long Short-Term Memory
Hochreiter, S.; and Schmidhuber, J. 1997 · 1997
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A Survey on Machine Reading Comprehension Systems
Baradaran, R.; Ghiasi, R.; and Amirkhani, H. 2020 · 2001
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Electra: Pre-training text encoders as discriminators rather than generators
Clark, K.; Luong, M.-T.; Le, Q. V.; and Manning, C. D. 2020 · 2003
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Multi-task Learning with Multi-head Attention for Multi-choice Reading Comprehension
Wan, H. 2020 · 2003
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UnifiedQA: Crossing format boundaries with a single QA system
Khashabi, D.; Khot, T.; Sabharwal, A.; Tafjord, O.; Clark, P.; and Hajishirzi, H. 2020 · 2005
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Neural Machine Translation by Jointly Learning to Align and Translate
Bahdanau, D.; Cho, K.; and Bengio, Y. 2015 · 2015
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Teaching Machines to Read and Comprehend
Hermann, K. M.; Kociský, T.; Grefenstette, E.; Espeholt, L.; Kay, W.; Suleyman, M.; and Blunsom, P. 2015 · 2015
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MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
Nguyen, T.; Rosenberg, M.; Song, X.; Gao, J.; Tiwary, S.; Majumder, R.; and Deng, L. 2016 · 2016
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SQuAD: 100,000+ Questions for Machine Comprehension of Text
Rajpurkar, P.; Zhang, J.; Lopyrev, K.; and Liang, P. 2016 · 2016
Cited alongside, same era.
RACE: Large-scale ReAding Comprehension Dataset From Examinations
Lai, G.; Xie, Q.; Liu, H.; Yang, Y.; and Hovy, E. 2017 · 2017
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Bidirectional Attention Flow for Machine Comprehension
Seo, M.; Kembhavi, A.; Farhadi, A.; and Hajishirzi, H. 2017 · 2017
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NewsQA: A machine comprehension dataset
Trischler, A.; Wang, T.; Yuan, X.; Harris, J.; Sordoni, A.; Bachman, P.; and Suleman, K. 2017 · 2017
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Attention Is All You Need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L.; and Polosukhin, I. 2017 · 2017
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Lingke: A Fine-grained Multi-turn Chatbot for Customer Service
Zhu, P.; Zhang, Z.; Li, J.; Huang, Y.; and Zhao, H. 2018b · 2018
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Convolutional spatial attention model for reading comprehension with multiple-choice questions
Chen, Z.; Cui, Y.; Ma, W.; Wang, S.; and Hu, G. 2019 · 2019
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Generating distractors for reading comprehension questions from real examinations
Gao, Y.; Bing, L.; Li, P.; King, I.; and Lyu, M. R. 2019 · 2019
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Read+ verify: Machine reading comprehension with unanswerable questions
Hu, M.; Wei, F.; Peng, Y.; Huang, Z.; Yang, N.; and Li, D. 2019 · 2019
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Improving Machine Reading Comprehension with General Reading Strategies
Sun, K.; Yu, D.; Yu, D.; and and, C. C. 2019b · 2019
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Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2018 · 2018
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Deep contextualized word representations
Peters, M.; Neumann, M.; Iyyer, M.; Gardner, M.; Clark, C.; Lee, K.; and Zettlemoyer, L. 2018 · 2018
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Improving language understanding with unsupervised learning
Radford, A.; Narasimhan, K.; Salimans, T.; and Sutskever, I. 2018 · 2018
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Multi-range Reasoning for Machine Comprehension
Tay, Y.; Tuan, L. A.; and Hui, S. C. 2018 · 2018
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A Co-Matching Model for Multi-choice Reading Comprehension
Wang, S.; Yu, M.; Chang, S.; and Jiang, J. 2018 · 2018
Cited alongside, same era.
Effective Character-augmented Word Embedding for Machine Reading Comprehension
Zhang, Z.; Huang, Y.; Zhu, P.; and Zhao, H. 2018a · 2018
Cited alongside, same era.
Modeling Multi-turn Conversation with Deep Utterance Aggregation
Zhang, Z.; Li, J.; Zhu, P.; Zhao, H.; and Liu, G. 2018b · 2018
Cited alongside, same era.
Multi-Matching Network for Multiple Choice Reading Comprehension
Tang, M.; Cai, J.; and Zhuo, H. H. 2019 · 2019
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A deep cascade model for multi-document reading comprehension
Yan, M.; Xia, J.; Wu, C.; Bi, B.; Zhao, Z.; Zhang, J.; Si, L.; Wang, R.; Wang, W.; and Chen, H. 2019 · 2019
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Translucent Answer Predictions in Multi-Hop Reading Comprehension
Bhargav, G. S.; Glass, M.; Garg, D.; Shevade, S.; Dana, S.; Khandelwal, D.; Subramaniam, L. V.; and Gliozzo, A. 2020 · 2020
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MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension
Jin, D.; Gao, S.; Kao, J.-Y.; Chung, T.; and Hakkani-tur, D. 2020 · 2020
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Learning to Classify the Wrong Answers for Multiple Choice Question Answering (Student Abstract)
Kim, H.; and Fung, P. 2020 · 2020
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ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
Lan, Z.; Chen, M.; Goodman, S.; Gimpel, K.; Sharma, P.; and Soricut, R. 2020 · 2020
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Select, Answer and Explain: Interpretable Multi-Hop Reading Comprehension over Multiple Documents
Tu, M.; Huang, K.; Wang, G.; Huang, J.; He, X.; and Zhou, B. 2020 · 2020
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DCMN+: Dual Co-Matching Network for Multi-choice Reading Comprehension
Zhang, S.; Zhao, H.; Wu, Y.; Zhang, Z.; Zhou, X.; and Zhou, X. 2020a · 2020
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