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Transformer architectures show significant promise for natural language processing.
On certain metric spaces arising from euclidean spaces by a change of metric and their imbedding in hilbert space
Isaac J Schoenberg · 1937
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Building a large annotated corpus of english: The penn treebank
Mitchell P. Marcus, Mary Ann Marcinkiewicz, and Beatrice Santorini · 1993
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A semantic concordance
George A. Miller, Claudia Leacock, Randee Tengi, and Ross Bunker · 1993
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Convolutional networks for images, speech, and time series
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Generating typed dependency parses from phrase structure parses
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Scikit-learn: Machine learning in Python
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Euclidean embeddings of finite metric spaces
Hiroshi Maehara · 2013
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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Pystanforddependencies
David McClosky · 2015
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Assessing the ability of lstms to learn syntax-sensitive dependencies
Tal Linzen, Emmanuel Dupoux, and Yoav Goldberg · 2016
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Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, and Rory Sayres · 2017
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Poincaré embeddings for learning hierarchical representations
Maximillian Nickel and Douwe Kiela · 2017
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Word sense disambiguation: A unified evaluation framework and empirical comparison
Alessandro Raganato, Jose Camacho-Collados, and Roberto Navigli · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Umap: Uniform manifold approximation and projection for dimension reduction
Leland McInnes, John Healy, and James Melville · 2018
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Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer · 2018
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What do you learn from context? probing for sentence structure in contextualized word representations
Ian Tenney, Patrick Xia, Berlin Chen, Alex Wang, Adam Poliak, R Thomas McCoy, Najoung Kim, Benjamin Van Durme, Samuel R Bowman, Dipanjan Das, et al · 2018
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Activation atlas
Shan Carter, Zan Armstrong, Ludwig Schubert, Ian Johnson, and Chris Olah · 2019
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A structural probe for finding syntax in word representations
John Hewitt and Christopher D Manning · 2019
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Deep rnns encode soft hierarchical syntax
Terra Blevins, Omer Levy, and Luke Zettlemoyer · 2018
Cited alongside, same era.
What you can cram into a single vector: Probing sentence embeddings for linguistic properties
Alexis Conneau, German Kruszewski, Guillaume Lample, Loïc Barrault, and Marco Baroni · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Cited alongside, same era.
Linguistic knowledge and transferability of contextual representations
Nelson F Liu, Matt Gardner, Yonatan Belinkov, Matthew Peters, and Noah A Smith · 2019
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Bert rediscovers the classical nlp pipeline
Ian Tenney, Dipanjan Das, and Ellie Pavlick · 2019
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Visualizing attention in transformer-based language models
Jesse Vig · 2019
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Minimizing the maximum dot product among k unit vectors in an n-dimensional space
Yury · 2019
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