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Natural Language Inference (NLI) task requires an agent to determine the logical relationship between a natural language premise and a natural language hypothesis.
a natural language inference system
Yaroslav Fyodorov, Yoad Winter, and Nissim Francez · 2000
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Recognising Textual Entailment with Logical Inference
Johan Bos and Katja Markert · 2005
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An extended model of natural logic
Bill MacCartney and Christopher D. Manning · 2009
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Parsing Natural Scenes and Natural Language with Recursive Neural Networks
Richard Socher, Cliff Chiung-Yu Lin, Andrew Y Ng, and Christopher D Manning · 2011
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ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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ADADELTA: An Adaptive Learning Rate Method
Matthew D Zeiler · 2012
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Distributed Representations of Words and Phrases and their Compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Gregory S Corrado, and Jeffrey Dean · 2013
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Neural Machine Translation by Jointly Learning to Align and Translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
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Convolutional Neural Network Architectures for Matching Natural Language Sentences
Baotian Hu, Zhengdong Lu, Hang Li, and Qingcai Chen · 2014
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A Convolutional Neural Network for Modelling Sentences
Nal Kalchbrenner, Edward Grefenstette, and Phil Blunsom · 2014
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Convolutional Neural Networks for Sentence Classification
Yoon Kim · 2014
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A SICK cure for the evaluation of compositional distributional semantic models
Marco Marelli, Stefano Menini, Marco Baroni, Luisa Bentivogli, Raffaella Bernardi, and Roberto Zamparelli · 2014
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Glove: Global Vectors for Word Representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan and Andrew Zisserman · 2014
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Going Deeper with Convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2014
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A large annotated corpus for learning natural language inference
Samuel R Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning · 2015
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Teaching Machines to Read and Comprehend
Karl Moritz Hermann, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom · 2015
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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 · 2015
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Reasoning about Entailment with Neural Attention
Tim Rocktäschel, Edward Grefenstette, Karl Moritz Hermann, and Phil Blunsom · 2015
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A Neural Attention Model for Abstractive Sentence Summarization
Alexander M Rush, Sumit Chopra, and Jason Weston · 2015
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Order-Embeddings of Images and Language
Ivan Vendrov, Ryan Kiros, Sanja Fidler, and Raquel Urtasun · 2015
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Learning Natural Language Inference with LSTM
Shuohang Wang and Jing Jiang · 2015
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Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Coherent Dialogue with Attention-based Language Models
Hongyuan Mei, Mohit Bansal, and Matthew R Walter · 2016
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Neural Semantic Encoders
Tsendsuren Munkhdalai and Hong Yu · 2016
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A Decomposable Attention Model for Natural Language Inference
Ankur P Parikh, Oscar Täckström, Dipanjan Das, and Jakob Uszkoreit · 2016
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Bidirectional Attention Flow for Machine Comprehension
Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi · 2016
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Reading and Thinking - Re-read LSTM Unit for Textual Entailment Recognition
Lei Sha, Baobao Chang, Zhifang Sui, and Sujian Li · 2016
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Refining Raw Sentence Representations for Textual Entailment Recognition via Attention
Jorge A Balazs, Edison Marrese-Taylor, Pablo Loyola, and Yutaka Matsuo · 2017
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Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard Zemel, and Yoshua Bengio · 2015
Cited alongside, same era.
Character-level Convolutional Networks for Text Classification
Xiang Zhang, Junbo Jake Zhao, and Yann LeCun · 2015
Cited alongside, same era.
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mane, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viegas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2016
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
Cited alongside, same era.
Enhanced LSTM for Natural Language Inference
Qian Chen, Xiaodan Zhu, Zhenhua Ling, Si Wei, Hui Jiang, and Diana Inkpen · 2016
Cited alongside, same era.
Long Short-Term Memory-Networks for Machine Reading
Jianpeng Cheng, Li Dong, and Mirella Lapata · 2016
Cited alongside, same era.
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
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Convolutional Sequence to Sequence Learning
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N Dauphin · 2017
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Ruminating Reader: Reasoning with Gated Multi-Hop Attention
Yichen Gong and Samuel R Bowman · 2017
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The RepEval 2017 Shared Task: Multi-Genre Natural Language Inference with Sentence Representations
Nikita Nangia, Adina Williams, Angeliki Lazaridou, and Samuel R Bowman · 2017
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Shortcut-Stacked Sentence Encoders for Multi-Domain Inference
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Neural Paraphrase Identification of Questions with Noisy Pretraining
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Attention Is All You Need
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Xiang Zhang and Yann LeCun · 2017
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