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Decoding for many NLP tasks requires an effective heuristic algorithm for approximating exact search since the problem of searching the full output space is often intractable, or impractical in many settings.
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Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
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BLEU: A method for automatic evaluation of machine translation
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A* parsing: Fast exact Viterbi parse selection
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ROUGE: A package for automatic evaluation of summaries
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Max-margin markov networks
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Beam-stack search: Integrating backtracking with beam search
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Parsing the SynTagRus treebank of Russian
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Hierarchical search for parsing
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Wit 3 : Web inventory of transcribed and translated talks
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Iterative Viterbi A* algorithm for k-best sequential decoding
Zhiheng Huang, Yi Chang, Bo Long, Jean-Francois Crespo, Anlei Dong, Sathiya Keerthi, and Su-Lin Wu. 2012 · 2012
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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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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
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Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
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Oriol Vinyals and Quoc V. Le. 2015 · 2015
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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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Six challenges for neural machine translation
Philipp Koehn and Rebecca Knowles. 2017 · 2017
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Multiresolution recurrent neural networks: An application to dialogue response generation
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Attention is all you need
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Kevin Duh. 2018 · 2018
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Understanding back-translation at scale
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Improved neural machine translation with SMT features
Wei He, Zhongjun He, Hua Wu, and Haifeng Wang. 2016 · 2016
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A diversity-promoting objective function for neural conversation models
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Minimum risk training for neural machine translation
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Speed-accuracy tradeoffs in tagging with variable-order CRFs and structured sparsity
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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, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Gregory S. Corrado, Macduff Hughes, and Jeffrey Dean. 2016 · 2016
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Correcting length bias in neural machine translation
Kenton Murray 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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Improving beam search by removing monotonic constraint for neural machine translation
Raphael Shu and Hideki Nakayama. 2018 · 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, Ryan Sepassi, Noam Shazeer, and Jakob Uszkoreit. 2018 · 2018
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Breaking the beam search curse: A study of (re-)scoring methods and stopping criteria for neural machine translation
Yilin Yang, Liang Huang, and Mingbo Ma. 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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On NMT search errors and model errors: Cat got your tongue?
Felix Stahlberg and Bill Byrne. 2019 · 2019
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XLNet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019 · 2019
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