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The Fact Extraction and VERification (FEVER) shared task was launched to support the development of systems able to verify claims by extracting supporting or refuting facts from raw text.
Framewise phoneme classification with bidirectional LSTM and other neural network architectures
Alex Graves and Jürgen Schmidhuber. 2005 · 2005
Earlier work this paper cites.
Large-Scale Named Entity Disambiguation Based on Wikipedia Data
Silviu Cucerzan. 2007 · 2007
Earlier work this paper cites.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
Earlier work this paper cites.
A large annotated corpus for learning natural language inference
Samuel R Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning. 2015 · 2015
Cited alongside, same era.
Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2016 · 2016
Cited alongside, same era.
Enhanced LSTM for natural language inference
Qian Chen, Xiaodan Zhu, Zhenhua Ling, Si Wei, Hui Jiang, and Diana Inkpen. 2016 · 2016
Cited alongside, same era.
AllenNLP: A deep semantic natural language processing platform
Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson F. Liu, Matthew Peters, Michael Schmitz, and Luke S. Zettlemoyer. 2017 · 2017
Later among the works it cites.
Mixing Context Granularities for Improved Entity Linking on Question Answering Data across Entity Categories
Daniil Sorokin and Iryna Gurevych. 2018 · 2018
Closest in time.
FEVER: A large-scale dataset for fact extraction and verification
James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018 · 2018
Closest in time.
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