Fetching the paper…
Reading the bibliography…
We consider the problem of adapting neural paragraph-level question answering models to the case where entire documents are given as input.
The TREC-8 Question Answering Track Report
Ellen M Voorhees et al. 1999 · 1999
Earlier work this paper cites.
Clueweb09 Data Set
Jamie Callan, Mark Hoy, Changkuk Yoo, and Le Zhao. 2009 · 2009
Earlier work this paper cites.
Adadelta: an adaptive learning rate method
Matthew D Zeiler. 2012 · 2012
Earlier work this paper cites.
Semantic Parsing on Freebase from Question-Answer Pairs
Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 2013 · 2013
Earlier work this paper cites.
Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
GloVe: Global Vectors for Word Representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
Earlier work this paper cites.
YodaQA: A Modular Question Answering System Pipeline
Petr Baudiš. 2015 · 2015
Earlier work this paper cites.
Teaching Machines to Read and Comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Earlier work this paper cites.
The Goldilocks Principle: Reading Children’s Books with Explicit Memory Representations
Felix Hill, Antoine Bordes, Sumit Chopra, and Jason Weston. 2015 · 2015
Earlier work this paper cites.
Long Short-Term Memory-Networks for Machine Reading
Jianpeng Cheng, Li Dong, and Mirella Lapata. 2016 · 2016
Cited alongside, same era.
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
Yarin Gal and Zoubin Ghahramani. 2016 · 2016
Cited alongside, same era.
Wikireading: A Novel Large-scale Language Understanding Task over Wikipedia
Daniel Hewlett, Alexandre Lacoste, Llion Jones, Illia Polosukhin, Andrew Fandrianto, Jay Han, Matthew Kelcey, and David Berthelot. 2016 · 2016
Cited alongside, same era.
Text understanding with the attention sum reader network
Rudolf Kadlec, Martin Schmid, Ondrej Bajgar, and Jan Kleindienst. 2016 · 2016
Cited alongside, same era.
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
Reading Wikipedia to Answer Open-Domain Questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017 · 2017
Closest in time.
Quasar: Datasets for Question Answering by Search and Reading
Bhuwan Dhingra, Kathryn Mazaitis, and William W Cohen. 2017 · 2017
Closest in time.
Mnemonic Reader: Machine Comprehension with Iterative Aligning and Multi-hop Answer Pointing
Minghao Hu, Yuxing Peng, and Xipeng Qiu. 2017 · 2017
Closest in time.
Adversarial Examples for Evaluating Reading Comprehension Systems
Robin Jia and Percy Liang. 2017 · 2017
Closest in time.
TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016 · 2016
Cited alongside, same era.
SQuAD: 100,000+ Questions for Machine Comprehension of Text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
Cited alongside, same era.
Bidirectional Attention Flow for Machine Comprehension
Min Joon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2016 · 2016
Cited alongside, same era.
Machine Comprehension Using Match-LSTM and Answer Pointer
Shuohang Wang and Jing Jiang. 2016 · 2016
Cited alongside, same era.
R: Reinforced Reader-Ranker for Open-Domain Question Answering
Shuohang Wang, Mo Yu, Xiaoxiao Guo, Zhiguo Wang, Tim Klinger, Wei Zhang, Shiyu Chang, Gerald Tesauro, Bowen Zhou, and Jing Jiang. 2017a
Cited in the paper.
Gated self-matching networks for reading comprehension and question answering
Wenhui Wang, Nan Yang, Furu Wei, Baobao Chang, and Ming Zhou. 2017b
Cited in the paper.
Dynamic Integration of Background Knowledge in Neural NLU Systems
Dirk Weissenborn, Tomáš Kočiský, and Chris Dyer. 2017a
Cited in the paper.
FastQA: A Simple and Efficient Neural Architecture for Question Answering
Dirk Weissenborn, Georg Wiese, and Laura Seiffe. 2017b
Cited in the paper.
Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer. 2017 · 2017
Closest in time.
Question Answering through Transfer Learning from Large Fine-grained Supervision Data
Sewon Min, Minjoon Seo, and Hannaneh Hajishirzi. 2017 · 2017
Closest in time.
MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension
Boyuan Pan, Hao Li, Zhou Zhao, Bin Cao, Deng Cai, and Xiaofei He. 2017 · 2017
Closest in time.
S-net: From answer extraction to answer generation for machine reading comprehension
Chuanqi Tan, Furu Wei, Nan Yang, Weifeng Lv, and Ming Zhou. 2017 · 2017
Closest in time.