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The attention layer in a neural network model provides insights into the model's reasoning behind its prediction, which are usually criticized for being opaque.
A Coefficient of Agreement for Nominal Scales
J. Cohen · 1960
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The measurement of observer agreement for categorical data
J. Richard Landis and Gary G. Koch · 1977
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts · 2011
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts · 2013
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Multiple object recognition with visual attention
Jimmy Ba, Volodymyr Mnih, and Koray Kavukcuoglu · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning · 2014
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Neural machine translation by jointly learning to align and translate
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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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Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom · 2015
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Effective approaches to attention-based neural machine translation
Thang Luong, Hieu Pham, and Christopher D. Manning · 2015
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Towards ai-complete question answering: A set of prerequisite toy tasks
Jason Weston, Antoine Bordes, Sumit Chopra, and Tomas Mikolov · 2015
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Show, attend and tell: Neural image caption generation with visual attention
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Multi30k: Multilingual english-german image descriptions
Desmond Elliott, Stella Frank, Khalil Sima’an, and Lucia Specia · 2016
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Reasoning about entailment with neural attention
Tim Rocktäschel, Edward Grefenstette, Karl Moritz Hermann, Tomas Kocisky, and Phil Blunsom · 2016
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Attention-based LSTM for aspect-level sentiment classification
Yequan Wang, Minlie Huang, Xiaoyan Zhu, and Li Zhao · 2016
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Hierarchical attention networks for document classification
Interpreting recurrent and attention-based neural models: a case study on natural language inference
Reza Ghaeini, Xiaoli Z. Fern, and Prasad Tadepalli · 2018
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Learning structured text representations
Yang Liu and Mirella Lapata · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman · 2018
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What does BERT look at? an analysis of bert’s attention
Kevin Clark, Urvashi Khandelwal, Omer Levy, and Christopher D. Manning · 2019
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Attention is not Explanation
Sarthak Jain and Byron C. Wallace · 2019
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Interpretability of deep learning models: A survey of results
S. Chakraborty, R. Tomsett, R. Raghavendra, D. Harborne, M. Alzantot, F. Cerutti, M. Srivastava, A. Preece, S. Julier, R. M. Rao, T. D. Kelley, D. Braines, M. Sensoy, C. J. Willis, and P. Gurram · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
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