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Neural machine translation (MT) models obtain state-of-the-art performance while maintaining a simple, end-to-end architecture.
Distributed representations, simple recurrent networks, and grammatical structure
Jeffrey L Elman. 1991 · 1991
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Part-of-Speech Tagging with Neural Networks
Helmut Schmid. 1994 · 1994
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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Maximum Entropy Models for Natural Language Ambiguity Resolution
Adwait Ratnaparkhi. 1998 · 1998
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A Hybrid Morpheme-Word Representation for Machine Translation of Morphologically Rich Languages
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WIT 3 : Web Inventory of Transcribed and Translated Talks
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Efficient Higher-Order CRFs for Morphological Tagging
Thomas Mueller, Helmut Schmid, and Hinrich Schütze. 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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Investigating the Usefulness of Generalized Word Representations in SMT
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Diederik Kingma and Jimmy Ba. 2014 · 2014
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MADAMIRA: A Fast, Comprehensive Tool for Morphological Analysis and Disambiguation of Arabic
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Ilya Sutskever, Oriol Vinyals, and Quoc VV Le. 2014 · 2014
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Seq2seq-attn
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Minh-Thang Luong and D. Christopher Manning. 2016 · 2016
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Does String-Based Neural MT Learn Source Syntax?
Xing Shi, Inkit Padhi, and Kevin Knight. 2016 · 2016
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