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We present MLQE-PE, a new dataset for Machine Translation (MT) Quality Estimation (QE) and Automatic Post-Editing (APE).
WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia
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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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Y. Tang, C. Tran, Xian Li, P. Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, and Angela Fan. 2020 · 2008
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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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Findings of the 2014 workshop on statistical machine translation
Ondrej Bojar, Christian Buck, Christian Federmann, Barry Haddow, Philipp Koehn, Johannes Leveling, Christof Monz, Pavel Pecina, Matt Post, Herve Saint-Amand, Radu Soricut, Lucia Specia, and Aleš Tamchyna. 2014 · 2014
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Predictor-Estimator using Multilevel Task Learning with Stack Propagation for Neural Quality Estimation
Hyun Kim, Jong-Hyeok Lee, and Seung-Hoon Na. 2017 · 2017
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Scaling neural machine translation
Myle Ott, Sergey Edunov, David Grangier, and Michael Auli. 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. 2018a · 2018
Cited alongside, same era.
Findings of the wmt 2019 shared task on automatic post-editing
Rajen Chatterjee, Christian Federmann, Matteo Negri, and Marco Turchi. 2019 · 2019
Cited alongside, same era.
ParaCrawl: Web-scale parallel corpora for the languages of the EU
Miquel Esplà, Mikel Forcada, Gema Ramírez-Sánchez, and Hieu Hoang. 2019 · 2019
Cited alongside, same era.
OpenKiwi: An open source framework for quality estimation
Fabio Kepler, Jonay Trénous, Marcos Treviso, Miguel Vera, and André F. T. Martins. 2019 · 2019
Later among the works it cites.
Facebook fair wmt19 news translation task submission
Nathan Ng, Kyra Yee, Alexei Baevski, Myle Ott, Michael Auli, and Sergey Edunov. 2019 · 2019
Later among the works it cites.
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
Later among the works it cites.
Unsupervised cross-lingual representation learning at scale
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020 · 2020
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Multi-hypothesis machine translation evaluation
Marina Fomicheva, Lucia Specia, and Francisco Guzmán. 2020 · 2020
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Findings of the WMT 2019 shared tasks on quality estimation
Erick Fonseca, Lisa Yankovskaya, André F. T. Martins, Mark Fishel, and Christian Federmann. 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.
Quality Estimation for Machine Translation
Lucia Specia, Carolina Scarton, and Gustavo Henrique Paetzold. 2018b
Cited in the paper.
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Simalign: High quality word alignments without parallel training data using static and contextualized embeddings
Masoud Jalili Sabet, Philipp Dufter, François Yvon, and Hinrich Schütze. 2020 · 2020
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Findings of the WMT 2020 shared task on quality estimation
Lucia Specia, Frédéric Blain, Marina Fomicheva, Erick Fonseca, Vishrav Chaudhary, Francisco Guzmán, and André F. T. Martins. 2020 · 2020
Closest in time.