One-shot and few-shot learning of word embeddings

Balanced Latin Squares, 1980

G. Campbell and S. Geller · 1980

Earlier work this paper cites.

Learning and connectionist representations

David E Rumelhart and Peter M Todd · 1993

Earlier work this paper cites.

Semantic Cognition: A Parallel Distributed Processing Approach

Timothy T Rogers and James L. McClelland · 2004

Earlier work this paper cites.

Building a large annotated corpus of English: The Penn Treebank

Mitchell P Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz · 2010

Earlier work this paper cites.

Linguistic regularities in continuous space word representations

Tomas Mikolov, Wen-tau Yih, and Geoffrey Zweig · 2013

Earlier work this paper cites.

GloVe: Global Vectors for Word Representation

Jeffrey Pennington, Richard Socher, and Christopher D Manning · 2014

Earlier work this paper cites.

Recurrent Neural Network Regularization

Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals · 2014

Cited alongside, same era.

Human-level control through deep reinforcement learning

Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei a Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015

Cited alongside, same era.

Morphological Smoothing and Extrapolation of Word Embeddings

Ryan Cotterell, Hinrich Schuetze, and Jason Eisner · 2016

Cited alongside, same era.

A Paradigm for Situated and Goal-Driven Language Learning

Jon Gauthier and Igor Mordatch · 2016

Cited alongside, same era.

What Learning Systems do Intelligent Agents Need ? Complementary Learning Systems Theory Updated

Dharshan Kumaran, Demis Hassabis, and James L McClelland · 2016

Cited alongside, same era.

Matching Networks for One Shot Learning

Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean · 2016

Later among the works it cites.

High-risk learning: acquiring new word vectors from tiny data

Original

Aurelie Herbelot and Marco Baroni · 2017

Closest in time.

Grounded Language Learning in a Simulated 3D World

Karl Moritz Hermann, Felix Hill, Simon Green, Fumin Wang, Ryan Faulkner, Hubert Soyer, David Szepesvari, Wojciech Marian Czarnecki, Max Jaderberg, Denis Teplyashin, Marcus Wainwright, Chris Apps, and Demis Hassabis · 2017

Closest in time.

Building Machines that learn and think like people

Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum, and Samuel J. Gershman · 2017

Closest in time.

Multimodal Word Meaning Induction From Minimal Exposure to Natural Text