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Building models that take advantage of the hierarchical structure of language without a priori annotation is a longstanding goal in natural language processing.
Recursive distributed representations
Jordan B Pollack. 1990 · 1990
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Learning task-dependent distributed representations by backpropagation through structure
Christoph Goller and Andreas Küchler. 1996 · 1996
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Stochastic inversion transduction grammars and bilingual parsing of parallel corpora
Dekai Wu. 1997 · 1997
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A hierarchical phrase-based model for statistical machine translation
David Chiang. 2005 · 2005
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Liang Huang, Kevin Knight, and Aravind Joshi. 2006 · 2006
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Learning continuous phrase representations and syntactic parsing with recursive neural networks
Richard Socher, Christopher Manning, and Andrew Ng. 2010 · 2010
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Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher Manning, Andrew Ng, and Christopher Potts. 2013 · 2013
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Adam: A method for stochastic optimization
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Learning to parse and translate improves neural machine translation
Akiko Eriguchi, Yoshimasa Tsuruoka, and Kyunghyun Cho. 2017 · 2017
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Neural machine translation with source-side latent graph parsing
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Structured attention networks
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Chris Dyer, Miguel Ballesteros, Wang Ling, Austin Matthews, and Noah A Smith. 2016a
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Recurrent neural network grammars
Chris Dyer, Adhiguna Kuncoro, Miguel Ballesteros, and Noah Smith. 2016b
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