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Quality Estimation (QE) is an important component in making Machine Translation (MT) useful in real-world applications, as it is aimed to inform the user on the quality of the MT output at test time.
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Confidence Estimation for Machine Translation
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Europarl: A parallel corpus for statistical machine translation
Philipp Koehn. 2005 · 2005
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A study of translation edit rate with targeted human annotation
Matthew Snover, Bonnie Dorr, Richard Schwartz, Linnea Micciulla, and John Makhoul. 2006 · 2006
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Estimating the sentence-level quality of machine translation systems
Lucia Specia, Marco Turchi, Nicola Cancedda, Marc Dymetman, and Nello Cristianini. 2009 · 2009
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Practical variational inference for neural networks
Alex Graves. 2011 · 2011
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Bayesian learning via stochastic gradient langevin dynamics
Max Welling and Yee W Teh. 2011 · 2011
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Quality estimation: an experimental study using unsupervised similarity measures
Erwan Moreau and Carl Vogel. 2012 · 2012
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Morpheme-and pos-based ibm1 scores and language model scores for translation quality estimation
Maja Popović. 2012 · 2012
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Continuous measurement scales in human evaluation of machine translation
Yvette Graham, Timothy Baldwin, Alistair Moffat, and Justin Zobel. 2013 · 2013
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QuEst-A Translation Quality Estimation Framework
Lucia Specia, Kashif Shah, José G. C. De Souza, and Trevor Cohn. 2013 · 2013
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Meteor universal: Language specific translation evaluation for any target language
Michael Denkowski and Alon Lavie. 2014 · 2014
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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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Accurate evaluation of segment-level machine translation metrics
Yvette Graham, Timothy Baldwin, and Nitika Mathur. 2015a · 2015
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Calibrated structured prediction
Volodymyr Kuleshov and Percy S Liang. 2015 · 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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Khanh Nguyen and Brendan O’Connor. 2015 · 2015
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Neural versus phrase-based machine translation quality: a case study
Luisa Bentivogli, Arianna Bisazza, Mauro Cettolo, and Marcello Federico. 2016 · 2016
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Results of the WMT16 metrics shared task
Ondřej Bojar, Yvette Graham, Amir Kamran, and Miloš Stanojević. 2016 · 2016
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Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Yarin Gal and Zoubin Ghahramani. 2016 · 2016
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Ensemble learning for multi-source neural machine translation
Ekaterina Garmash and Christof Monz. 2016 · 2016
Results of the WMT18 metrics shared task: Both characters and embeddings achieve good performance
Qingsong Ma, Ondřej Bojar, and Yvette Graham. 2018 · 2018
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Uncertainty in neural networks: Bayesian ensembling
Tim Pearce, Mohamed Zaki, Alexandra Brintrup, and Andy Neel. 2018 · 2018
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Findings of the WMT 2018 Shared Task on Quality Estimation
Lucia Specia, Frédéric Blain, Varvara Logacheva, Ramón Astudillo, and André FT Martins. 2018 · 2018
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Alibaba submission for WMT18 quality estimation task
Jiayi Wang, Kai Fan, Bo Li, Fengming Zhou, Boxing Chen, Yangbin Shi, and Luo Si. 2018 · 2018
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Quality estimation with force-decoded attention and cross-lingual embeddings
Elizaveta Yankovskaya, Andre Tattar, and Mark Fishel. 2018 · 2018
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Is all that glitters in machine translation quality estimation really gold?
Yvette Graham, Timothy Baldwin, Meghan Dowling, Maria Eskevich, Teresa Lynn, and Lamia Tounsi. 2016 · 2016
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel. 2016 · 2016
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Diverse beam search: Decoding diverse solutions from neural sequence models
Ashwin K. Vijayakumar, Michael Cogswell, Ramprasath R. Selvaraju, Qing Sun, Stefan Lee, David Crandall, and Dhruv Batra. 2016 · 2016
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Results of the WMT17 metrics shared task
Ondřej Bojar, Yvette Graham, and Amir Kamran. 2017 · 2017
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Is neural machine translation the new state of the art?
Sheila Castilho, Joss Moorkens, Federico Gaspari, Iacer Calixto, John Tinsley, and Andy Way. 2017 · 2017
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On Calibration of Modern Neural Networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. 2017 · 2017
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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, Shervina Malmasi, Christof Monz, Mathias Müller, Santanu Pal, Matt Post, and Marcos Zampieri. 2019 · 2019
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Mixture content selection for diverse sequence generation
Jaemin Cho, Minjoon Seo, and Hannaneh Hajishirzi. 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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ParaCrawl: Web-scale parallel corpora for the languages of the EU
Miquel Esplà, Mikel Forcada, Gema Ramírez-Sánchez, and Hieu Hoang. 2019 · 2019
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Findings of the WMT 2019 Shared Tasks on Quality Estimation
Erick Fonseca, Lisa Yankovskaya, André FT Martins, Mark Fishel, and Christian Federmann. 2019 · 2019
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The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English
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Results of the WMT19 metrics shared task: Segment-level and strong MT systems pose big challenges
Qingsong Ma, Johnny Wei, Ondřej Bojar, and Yvette Graham. 2019 · 2019
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Prediction uncertainty estimation for hate speech classification
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Modeling confidence in sequence-to-sequence models
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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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Can you trust your model’s uncertainty? Evaluating predictive uncertainty under dataset shift
Jasper Snoek, Yaniv Ovadia, Emily Fertig, Balaji Lakshminarayanan, Sebastian Nowozin, D Sculley, Joshua Dillon, Jie Ren, and Zachary Nado. 2019 · 2019
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Bayesian layers: A module for neural network uncertainty
Dustin Tran, Mike Dusenberry, Mark van der Wilk, and Danijar Hafner. 2019 · 2019
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Analyzing the structure of attention in a transformer language model
Jesse Vig and Yonatan Belinkov. 2019 · 2019
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Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned
Elena Voita, David Talbot, Fedor Moiseev, Rico Sennrich, and Ivan Titov. 2019 · 2019
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Improving Back-Translation with Uncertainty-based Confidence Estimation
Shuo Wang, Yang Liu, Chao Wang, Huanbo Luan, and Maosong Sun. 2019 · 2019
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Quality in, quality out: Learning from actual mistakes
Frédéric Blain, Nikolaos Aletras, and Lucia Specia. 2020 · 2020
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