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Neural language models are a critical component of state-of-the-art systems for machine translation, summarization, audio transcription, and other tasks.
Exploiting syntactic structure for language modeling
Ciprian Chelba and Frederick Jelinek. 1998 · 1998
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A syntax-based statistical translation model
Kenji Yamada and Kevin Knight. 2001 · 2001
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Syntax-based language models for statistical machine translation
Eugene Charniak, Kevin Knight, and Kenji Yamada. 2003 · 2003
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Deep syntax language models and statistical machine translation
Yvette Graham and Josef Genabith. 2010 · 2010
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The sequence memoizer
Frank Wood, Jan Gasthaus, Cédric Archambeau, Lancelot James, and Yee Whye Teh. 2011 · 2011
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One billion word benchmark for measuring progress in statistical language modeling
Ciprian Chelba, Tomáš Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, and Phillipp Koehn. 2013 · 2013
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba. 2014 · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
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Globally normalized transition-based neural networks
Daniel Andor, Chris Alberti, David Weiss, Aliaksei Severyn, Alessandro Presta, Kuzman Ganchev, Slav Petrov, and Michael Collins. 2016 · 2016
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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
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Exploring the limits of language modeling
Rafal Jozefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, and Yonghui Wu. 2016 · 2016
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Marcin Junczys-Dowmunt and Roman Grundkiewicz. 2016 · 2016
Review networks for caption generation
Zhilin Yang, Ye Yuan, Yuexin Wu, William W Cohen, and Ruslan R Salakhutdinov. 2016 · 2016
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Learning to compose words into sentences with reinforcement learning
Dani Yogatama, Phil Blunsom, Chris Dyer, Edward Grefenstette, and Wang Ling. 2016 · 2016
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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 · 2017
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Deliberation networks: Sequence generation beyond one-pass decoding
Yingce Xia, Fei Tian, Lijun Wu, Jianxin Lin, Tao Qin, Nenghai Yu, and Tie-Yan Liu. 2017 · 2017
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MaskGAN: Better text generation via filling in the _______
William Fedus, Ian Goodfellow, and Andrew M. Dai. 2018 · 2018
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Pixel recurrent neural networks
Aaron Van Oord, Nal Kalchbrenner, and Koray Kavukcuoglu. 2016 · 2016
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Order matters: Sequence to sequence for sets
Oriol Vinyals, Samy Bengio, and Manjunath Kudlur. 2016 · 2016
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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, et al. 2016 · 2016
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Urvashi Khandelwal, He He, Peng Qi, and Dan Jurafsky. 2018 · 2018
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Neural language modeling by jointly learning syntax and lexicon
Yikang Shen, Zhouhan Lin, Chin-wei Huang, and Aaron Courville. 2018 · 2018
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