Fetching the paper…
Reading the bibliography…
Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), is one of the most important problems in natural language processing.
Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
Earlier work this paper cites.
Literature and cognition
Jerry R Hobbs. 1990 · 1990
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams. 1992 · 1992
Earlier work this paper cites.
Web based probabilistic textual entailment
Oren Glickman, Ido Dagan, and Moshe Koppel. 2005 · 2005
Earlier work this paper cites.
Natural logic for textual inference
Bill MacCartney and Christopher D Manning. 2007 · 2007
Earlier work this paper cites.
A unified architecture for natural language processing: Deep neural networks with multitask learning
Ronan Collobert and Jason Weston. 2008 · 2008
Earlier work this paper cites.
Adadelta: an adaptive learning rate method
Matthew D Zeiler. 2012 · 2012
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
Earlier work this paper cites.
The stanford corenlp natural language processing toolkit
Christopher Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven Bethard, and David McClosky. 2014 · 2014
Earlier work this paper cites.
A sick cure for the evaluation of compositional distributional semantic models
Marco Marelli, Stefano Menini, Marco Baroni, Luisa Bentivogli, Raffaella Bernardi, Roberto Zamparelli, et al. 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.
From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions
Peter Young, Alice Lai, Micah Hodosh, and Julia Hockenmaier. 2014 · 2014
Cited alongside, same era.
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.
Skip-thought vectors
Ryan Kiros, Yukun Zhu, Ruslan R Salakhutdinov, Richard Zemel, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015 · 2015
Cited alongside, same era.
Aligning books and movies: Towards story-like visual explanations by watching movies and reading books
Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015 · 2015
Cited alongside, same era.
A fast unified model for parsing and sentence understanding
Samuel R Bowman, Jon Gauthier, Abhinav Rastogi, Raghav Gupta, Christopher D Manning, and Christopher Potts. 2016 · 2016
Cited alongside, same era.
Supervised learning of universal sentence representations from natural language inference data
Alexis Conneau, Douwe Kiela, Holger Schwenk, Loïc Barrault, and Antoine Bordes. 2017 · 2017
Later among the works it cites.
Discourse-based objectives for fast unsupervised sentence representation learning
Yacine Jernite, Samuel R Bowman, and David Sontag. 2017 · 2017
Later among the works it cites.
Learned in translation: Contextualized word vectors
Bryan McCann, James Bradbury, Caiming Xiong, and Richard Socher. 2017 · 2017
Later among the works it cites.
Dissent: Sentence representation learning from explicit discourse relations
Allen Nie, Erin D Bennett, and Noah D Goodman. 2017 · 2017
Later among the works it cites.
Shortcut-stacked sentence encoders for multi-domain inference
Yixin Nie and Mohit Bansal. 2017 · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Natural language inference by tree-based convolution and heuristic matching
Lili Mou, Rui Men, Ge Li, Yan Xu, Lu Zhang, Rui Yan, and Zhi Jin. 2016 · 2016
Cited alongside, same era.
Reasoning about entailment with neural attention
Tim Rocktäschel, Edward Grefenstette, Karl Moritz Hermann, Tomáš Kočiskỳ, and Phil Blunsom. 2016 · 2016
Cited alongside, same era.
Bidirectional attention flow for machine comprehension
Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2016 · 2016
Cited alongside, same era.
Reading and thinking: Re-read lstm unit for textual entailment recognition
Lei Sha, Baobao Chang, Zhifang Sui, and Sujian Li. 2016 · 2016
Cited alongside, same era.
Order-embeddings of images and language
Ivan Vendrov, Ryan Kiros, Sanja Fidler, and Raquel Urtasun. 2016 · 2016
Cited alongside, same era.
Learning to compose task-specific tree structures
Jihun Choi, Kang Min Yoo, and Sang goo Lee. 2017 · 2017
Cited alongside, same era.
Reading wikipedia to answer open-domain questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017a
Cited in the paper.
Later among the works it cites.
A compare-propagate architecture with alignment factorization for natural language inference
Yi Tay, Luu Anh Tuan, and Siu Cheung Hui. 2017 · 2017
Later among the works it cites.
Bilateral multi-perspective matching for natural language sentences
Zhiguo Wang, Wael Hamza, and Radu Florian. 2017 · 2017
Later among the works it cites.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R Bowman. 2017 · 2017
Later among the works it cites.
Words or characters? fine-grained gating for reading comprehension
Zhilin Yang, Bhuwan Dhingra, Ye Yuan, Junjie Hu, William W. Cohen, and Ruslan Salakhutdinov. 2017 · 2017
Later among the works it cites.
What to do next: modeling user behaviors by time-lstm
Yu Zhu, Hao Li, Yikang Liao, Beidou Wang, Ziyu Guan, Haifeng Liu, and Deng Cai. 2017 · 2017
Later among the works it cites.
Natural language inference over interaction space
Yichen Gong, Heng Luo, and Jian Zhang. 2018 · 2018
Later among the works it cites.