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Low-resource language translation is a challenging but socially valuable NLP task.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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Statistical significance tests for machine translation evaluation
Philipp Koehn. 2004 · 2004
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The iwslt 2015 evaluation campaign
M. Cettolo, J. Niehues, S. Stüker, L. Bentivogli, R. Cattoni, and M. Federico. 2015 · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy. 2015 · 2015
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Layer normalization
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. 2016 · 2016
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The iwslt 2016 evaluation campaign
M. Cettolo, Niehues Jan, Stüker Sebastian, L. Bentivogli, R. Cattoni, and M. Federico. 2016 · 2016
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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Weighted transformer network for machine translation
Karim Ahmed, Nitish Shirish Keskar, and Richard Socher. 2017 · 2017
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OpenNMT: Open-source toolkit for neural machine translation
Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senellart, and Alexander Rush. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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The best of both worlds: Combining recent advances in neural machine translation
Mia Xu Chen, Orhan Firat, Ankur Bapna, Melvin Johnson, Wolfgang Macherey, George Foster, Llion Jones, Mike Schuster, Noam Shazeer, Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Zhifeng Chen, Yonghui Wu, and Macduff Hughes. 2018 · 2018
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How much attention do you need? a granular analysis of neural machine translation architectures
Tobias Domhan. 2018 · 2018
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Cosine Normalization: Using Cosine Similarity Instead of Dot Product in Neural Networks: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part I , pages 382–391
Chunjie Luo, Jianfeng Zhan, Xiaohe Xue, Lei Wang, and Rui Ren. 2018 · 2018
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Improving lexical choice in neural machine translation
Toan Nguyen and David Chiang. 2018 · 2018
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A call for clarity in reporting BLEU scores
Matt Post. 2018 · 2018
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When and why are pre-trained word embeddings useful for neural machine translation?
Ye Qi, Devendra Sachan, Matthieu Felix, Sarguna Padmanabhan, and Graham Neubig. 2018 · 2018
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fairseq: A fast, extensible toolkit for sequence modeling
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019 · 2019
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Learning deep transformer models for machine translation
Qiang Wang, Bei Li, Tong Xiao, Jingbo Zhu, Changliang Li, Derek F. Wong, and Lidia S. Chao. 2019 · 2019
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Understanding and improving layer normalization
Jingjing Xu, Xu Sun, Zhiyuan Zhang, Guangxiang Zhao, and Junyang Lin. 2019 · 2019
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Root mean square layer normalization
Biao Zhang and Rico Sennrich. 2019 · 2019
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Explicit sparse transformer: Concentrated attention through explicit selection
Guangxiang Zhao, Junyang Lin, Zhiyuan Zhang, Xuancheng Ren, Qi Su, and Xu Sun. 2019 · 2019
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Language model prior for low-resource neural machine translation
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Tensor2Tensor for neural machine translation
Ashish Vaswani, Samy Bengio, Eugene Brevdo, Francois Chollet, Aidan Gomez, Stephan Gouws, Llion Jones, Łukasz Kaiser, Nal Kalchbrenner, Niki Parmar, Ryan Sepassi, Noam Shazeer, and Jakob Uszkoreit. 2018 · 2018
Cited alongside, same era.
Accelerating neural transformer via an average attention network
Biao Zhang, Deyi Xiong, and Jinsong Su. 2018 · 2018
Cited alongside, same era.
Massively multilingual neural machine translation
Roee Aharoni, Melvin Johnson, and Orhan Firat. 2019 · 2019
Cited alongside, same era.
What does BERT look at? an analysis of BERT’s attention
Kevin Clark, Urvashi Khandelwal, Omer Levy, and Christopher D. Manning. 2019 · 2019
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Transformer-XL: Attentive language models beyond a fixed-length context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc Le, and Ruslan Salakhutdinov. 2019 · 2019
Cited alongside, same era.
The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English
Francisco Guzmán, Peng-Jen Chen, Myle Ott, Juan Pino, Guillaume Lample, Philipp Koehn, Vishrav Chaudhary, and Marc’Aurelio Ranzato. 2019 · 2019
Cited alongside, same era.
Transformers without tears: Improving the normalization of self-attention
Toan Q. Nguyen and Julian Salazar. 2019 · 2019
Cited alongside, same era.
Christos Baziotis, Barry Haddow, and Alexandra Birch. 2020 · 2020
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On optimal transformer depth for low-resource language translation
Elan van Biljon, Arnu Pretorius, and Julia Kreutzer. 2020 · 2020
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Stolen probability: A structural weakness of neural language models
David Demeter, Gregory Kimmel, and Doug Downey. 2020 · 2020
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BPE-dropout: Simple and effective subword regularization
Ivan Provilkov, Dmitrii Emelianenko, and Elena Voita. 2020 · 2020
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Normalized attention without probability cage
Oliver Richter and Roger Wattenhofer. 2020 · 2020
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Powernorm: Rethinking batch normalization in transformers
Sheng Shen, Zhewei Yao, Amir Gholami, Michael Mahoney, and Kurt Keutzer. 2020 · 2020
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On layer normalization in the transformer architecture
Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng, Chen Xing, Huishuai Zhang, Yanyan Lan, Liwei Wang, and Tie-Yan Liu. 2020 · 2020
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