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This paper tackles the problem of reading comprehension over long narratives where documents easily span over thousands of tokens.
Curriculum learning
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Teaching machines to read and comprehend
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Incorporating copying mechanism in sequence-to-sequence learning
Jiatao Gu, Zhengdong Lu, Hang Li, and Victor OK Li. 2016 · 2016
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Text understanding with the attention sum reader network
Rudolf Kadlec, Martin Schmid, Ondrej Bajgar, and Jan Kleindienst. 2016 · 2016
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Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Easy questions first? a case study on curriculum learning for question answering
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Bidirectional attention flow for machine comprehension
Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2016 · 2016
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Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman. 2016 · 2016
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Dynamic coattention networks for question answering
Caiming Xiong, Victor Zhong, and Richard Socher. 2016 · 2016
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Reading wikipedia to answer open-domain questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Attention-guided answer distillation for machine reading comprehension
Minghao Hu, Yuxing Peng, Furu Wei, Zhen Huang, Dongsheng Li, Nan Yang, and Ming Zhou. 2018 · 2018
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The narrativeqa reading comprehension challenge
Tomáš Kočiskỳ, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gáabor Melis, and Edward Grefenstette. 2018 · 2018
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Denoising distantly supervised open-domain question answering
Yankai Lin, Haozhe Ji, Zhiyuan Liu, and Maosong Sun. 2018 · 2018
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The natural language decathlon: Multitask learning as question answering
Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher. 2018 · 2018
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Christopher Clark and Matt Gardner. 2017 · 2017
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A deep reinforced model for abstractive summarization
Romain Paulus, Caiming Xiong, and Richard Socher. 2017 · 2017
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Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J Liu, and Christopher D Manning. 2017 · 2017
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S-net: From answer extraction to answer generation for machine reading comprehension
Chuanqi Tan, Furu Wei, Nan Yang, Bowen Du, Weifeng Lv, and Ming Zhou. 2017 · 2017
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Domain randomization for transferring deep neural networks from simulation to the real world
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Recurrently controlled recurrent networks
Yi Tay, Anh Tuan Luu, and Siu Cheung Hui. 2018b
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Densely connected attention propagation for reading comprehension
Yi Tay, Anh Tuan Luu, Siu Cheung Hui, and Jian Su. 2018c
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Efficient and robust question answering from minimal context over documents
Sewon Min, Victor Zhong, Richard Socher, and Caiming Xiong. 2018 · 2018
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Multi-granular sequence encoding via dilated compositional units for reading comprehension
Yi Tay, Anh Tuan Luu, and Siu Cheung Hui. 2018a · 2018
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R 3: Reinforced ranker-reader for open-domain question answering
Shuohang Wang, Mo Yu, Xiaoxiao Guo, Zhiguo Wang, Tim Klinger, Wei Zhang, Shiyu Chang, Gerry Tesauro, Bowen Zhou, and Jing Jiang. 2018 · 2018
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Show, attend and tell: Neural image caption generation with visual attention
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