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The transformer has been shown to outperform recurrent neural network-based sequence-to-sequence models in various word-level NLP tasks.
Parallel networks that learn to pronounce English text
Terrence J. Sejnowski and Charles R. Rosenberg. 1987 · 1987
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The Carnegie Mellon pronouncing dictionary
R.L. Weide. 1998 · 1998
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An SMT approach to automatic annotation of historical text
Eva Pettersson, Beáta Megyesi, and Jörg Tiedemann. 2013 · 2013
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Felipe Sánchez-Martínez, Isabel Martínez-Sempere, Xavier Ivars-Ribes, and Rafael C. Carrasco. 2013 · 2013
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Modernizing historical Slovene words with character-based smt
Yves Scherrer and Tomaž Erjavec. 2013 · 2013
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2014 · 2014
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Morphological inflection generation with hard monotonic attention
Roee Aharoni and Yoav Goldberg. 2017 · 2015
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Effective approaches to attention-based neural machine translation
Thang Luong, Hieu Pham, and Christopher D. Manning. 2015 · 2015
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Whitepaper of NEWS 2015 shared task on machine transliteration
Min Zhang, Haizhou Li, Rafael E. Banchs, and A Kumaran. 2015 · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. 2016 · 2016
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The SIGMORPHON 2016 shared Task—Morphological reinflection
Ryan Cotterell, Christo Kirov, John Sylak-Glassman, David Yarowsky, Jason Eisner, and Mans Hulden. 2016 · 2016
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Normalising Slovene data: historical texts vs. user-generated content
Nikola Ljubešic, Katja Zupan, Darja Fišer, and Tomaz Erjavec. 2016 · 2016
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Normalising Slovene data: historical texts vs. user-generated content
Nikola Ljubešić, Katja Zupan, Darja Fišer, and Tomaž Erjavec. 2016 · 2016
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Spelling normalisation and linguistic analysis of historical text for information extraction
Eva Pettersson. 2016 · 2016
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Modernising historical Slovene words
Yves Scherrer and Tomaž Erjavec. 2016 · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016 · 2016
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Training data augmentation for low-resource morphological inflection
Toms Bergmanis, Katharina Kann, Hinrich Schütze, and Sharon Goldwater. 2017 · 2017
Cited alongside, same era.
CoNLL-SIGMORPHON 2017 shared task: Universal morphological reinflection in 52 languages
Ryan Cotterell, Christo Kirov, John Sylak-Glassman, Géraldine Walther, Ekaterina Vylomova, Patrick Xia, Manaal Faruqui, Sandra Kübler, David Yarowsky, Jason Eisner, and Mans Hulden. 2017 · 2017
Cited alongside, same era.
Convolutional sequence to sequence learning
Why self-attention? a targeted evaluation of neural machine translation architectures
Gongbo Tang, Mathias Müller, Annette Rios, and Rico Sennrich. 2018b · 2018
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Tensor2tensor for neural machine translation
Ashish Vaswani, Samy Bengio, Eugene Brevdo, Francois Chollet, Aidan N Gomez, Stephan Gouws, Llion Jones, Łukasz Kaiser, Nal Kalchbrenner, Niki Parmar, et al. 2018 · 2018
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Hard non-monotonic attention for character-level transduction
Shijie Wu, Pamela Shapiro, and Ryan Cotterell. 2018 · 2018
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Pushing the limits of low-resource morphological inflection
Antonios Anastasopoulos and Graham Neubig. 2019 · 2019
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Findings of the 2019 conference on machine translation (WMT19)
Loïc Barrault, Ondřej Bojar, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Philipp Koehn, Shervin Malmasi, Christof Monz, Mathias Müller, Santanu Pal, Matt Post, and Marcos Zampieri. 2019 · 2019
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Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin. 2017 · 2017
Cited alongside, same era.
Align and copy: UZH at SIGMORPHON 2017 shared task for morphological reinflection
Peter Makarov, Tatiana Ruzsics, and Simon Clematide. 2017 · 2017
Cited alongside, same era.
Data augmentation for morphological reinflection
Miikka Silfverberg, Adam Wiemerslage, Ling Liu, and Lingshuang Jack Mao. 2017 · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Normalization of historical texts with neural network models
Marcel Bollmann. 2018 · 2018
Cited alongside, same era.
The CoNLL–SIGMORPHON 2018 shared task: Universal morphological reinflection
Ryan Cotterell, Christo Kirov, John Sylak-Glassman, Géraldine Walther, Ekaterina Vylomova, Arya D. McCarthy, Katharina Kann, Sebastian Mielke, Garrett Nicolai, Miikka Silfverberg, David Yarowsky, Jason Eisner, and Mans Hulden. 2018 · 2018
Cited alongside, same era.
Training tips for the transformer model
Martin Popel and Ondřej Bojar. 2018 · 2018
Cited alongside, same era.
Later among the works it cites.
A large-scale comparison of historical text normalization systems
Marcel Bollmann. 2019 · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Unified language model pre-training for natural language understanding and generation
Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2019 · 2019
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Historical text normalization with delayed rewards
Simon Flachs, Marcel Bollmann, and Anders Søgaard. 2019 · 2019
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Weird inflects but OK: Making sense of morphological generation errors
Kyle Gorman, Arya D. McCarthy, Ryan Cotterell, Ekaterina Vylomova, Miikka Silfverberg, and Magdalena Markowska. 2019 · 2019
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The SIGMORPHON 2019 shared task: Morphological analysis in context and cross-lingual transfer for inflection
Arya D. McCarthy, Ekaterina Vylomova, Shijie Wu, Chaitanya Malaviya, Lawrence Wolf-Sonkin, Garrett Nicolai, Christo Kirov, Miikka Silfverberg, Sebastian J. Mielke, Jeffrey Heinz, Ryan Cotterell, and Mans Hulden. 2019 · 2019
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Exact hard monotonic attention for character-level transduction
Shijie Wu and Ryan Cotterell. 2019 · 2019
Later among the works it cites.