Fetching the paper…
Reading the bibliography…
Neural Machine Translation (NMT) currently exhibits biases such as producing translations that are too short and overgenerating frequent words, and shows poor robustness to copy noise in training data or domain shift.
Calibration of encoder decoder models for neural machine translation
Aviral Kumar and Sunita Sarawagi. 2019 · 1903
Earlier work this paper cites.
Minimum bayes-risk automatic speech recognition
Vaibhava Goel and William J Byrne. 2000 · 2000
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.
Minimum Bayes-risk decoding for statistical machine translation
Shankar Kumar and William Byrne. 2004 · 2004
Earlier work this paper cites.
Lattice Minimum Bayes-Risk decoding for statistical machine translation
Roy Tromble, Shankar Kumar, Franz Och, and Wolfgang Macherey. 2008 · 2008
Earlier work this paper cites.
Linguistic Structure Prediction
Noah A. Smith. 2011 · 2011
Earlier work this paper cites.
Sequence transduction with recurrent neural networks
Alex Graves. 2012 · 2012
Earlier work this paper cites.
Optimizing for sentence-level BLEU+1 yields short translations
Preslav Nakov, Francisco Guzman, and Stephan Vogel. 2012 · 2012
Earlier work this paper cites.
Parallel data, tools and interfaces in OPUS
Jörg Tiedemann. 2012 · 2012
Earlier work this paper cites.
Audio chord recognition with recurrent neural networks
Nicolas Boulanger-Lewandowski, Yoshua Bengio, and Pascal Vincent. 2013 · 2013
Earlier work this paper cites.
A systematic comparison of smoothing techniques for sentence-level BLEU
Boxing Chen and Colin Cherry. 2014 · 2014
Earlier work this paper cites.
Meteor universal: Language specific translation evaluation for any target language
Michael Denkowski and Alon Lavie. 2014 · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014 · 2014
Earlier work this paper cites.
Scheduled sampling for sequence prediction with recurrent neural networks
Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer. 2015 · 2015
Cited alongside, same era.
chrF deconstructed: beta parameters and n-gram weights
Maja Popović. 2016 · 2016
Cited alongside, same era.
Minimum risk training for neural machine translation
Shiqi Shen, Yong Cheng, Zhongjun He, Wei He, Hua Wu, Maosong Sun, and Yang Liu. 2016 · 2016
Cited alongside, same era.
Sequence-to-sequence learning as beam-search optimization
Sam Wiseman and Alexander M. Rush. 2016 · 2016
Cited alongside, same era.
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, et al. 2016 · 2016
Cited alongside, same era.
Hallucinations in neural machine translation
Katherine Lee, Orhan Firat, Ashish Agarwal, Clara Fannjiang, and David Sussillo. 2018 · 2018
Later among the works it cites.
Analyzing uncertainty in neural machine translation
Myle Ott, Michael Auli, David Grangier, and Marc’Aurelio Ranzato. 2018 · 2018
Later among the works it cites.
A call for clarity in reporting BLEU scores
Matt Post. 2018 · 2018
Later among the works it cites.
On NMT search errors and model errors: Cat got your tongue?
Felix Stahlberg and Bill Byrne. 2019 · 2019
Later among the works it cites.
Bridging the gap between training and inference for neural machine translation
Wen Zhang, Yang Feng, Fandong Meng, Di You, and Qun Liu. 2019 · 2019
Later among the works it cites.
ParaCrawl: Web-scale acquisition of parallel corpora
Marta Bañón, Pinzhen Chen, Barry Haddow, Kenneth Heafield, Hieu Hoang, Miquel Esplà-Gomis, Mikel L. Forcada, Amir Kamran, Faheem Kirefu, Philipp Koehn, Sergio Ortiz Rojas, Leopoldo Pla Sempere, Gema Ramírez-Sánchez, Elsa Sarrías, Marek Strelec, Brian Thompson, William Waites, Dion Wiggins, and Jaume Zaragoza. 2020 · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Frédéric Blain, Pranava Swaroop Madhyastha, and Lucia Specia. 2017 · 2017
Cited alongside, same era.
Six challenges for neural machine translation
Philipp Koehn and Rebecca Knowles. 2017 · 2017
Cited alongside, same era.
Later-stage minimum bayes-risk decoding for neural machine translation
Raphael Shu and Hideki Nakayama. 2017 · 2017
Cited alongside, same era.
Neural machine translation by minimising the Bayes-risk with respect to syntactic translation lattices
Felix Stahlberg, Adrià de Gispert, Eva Hasler, and Bill Byrne. 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.
Dual conditional cross-entropy filtering of noisy parallel corpora
Marcin Junczys-Dowmunt. 2018 · 2018
Cited alongside, same era.
On the impact of various types of noise on neural machine translation
Huda Khayrallah and Philipp Koehn. 2018 · 2018
Cited alongside, same era.
Later among the works it cites.
The sockeye 2 neural machine translation toolkit at AMTA 2020
Tobias Domhan, Michael Denkowski, David Vilar, Xing Niu, Felix Hieber, and Kenneth Heafield. 2020 · 2020
Later among the works it cites.
Is MAP decoding all you need? the inadequacy of the mode in neural machine translation
Bryan Eikema and Wilker Aziz. 2020 · 2020
Later among the works it cites.
Domain robustness in neural machine translation
Mathias Müller, Annette Rios, and Rico Sennrich. 2020 · 2020
Later among the works it cites.
SSMBA: Self-supervised manifold based data augmentation for improving out-of-domain robustness
Nathan Ng, Kyunghyun Cho, and Marzyeh Ghassemi. 2020 · 2020
Later among the works it cites.
COMET: A neural framework for MT evaluation
Ricardo Rei, Craig Stewart, Ana C Farinha, and Alon Lavie. 2020 · 2020
Later among the works it cites.
OPUS-MT — Building open translation services for the World
Jörg Tiedemann and Santhosh Thottingal. 2020 · 2020
Later among the works it cites.
The tatoeba translation challenge – realistic data sets for low resource and multilingual mt
Jörg Tiedemann. 2020 · 2020
Later among the works it cites.