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Automatic post-editing (APE) aims to improve machine translations, thereby reducing human post-editing effort.
HuggingFace’s Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, R’emi Louf, Morgan Funtowicz, and Jamie Brew. 2019 · 1910
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A coefficient of agreement for nominal scales
Jacob Cohen. 1960 · 1960
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Weighted Kappa: Nominal scale agreement provision for scaled disagreement or partial credit
Jacob Cohen. 1968 · 1968
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The measurement of observer agreement for categorical dat
J. Richard Landis and Gary G. Koch. 1977 · 1977
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Automated postediting of documents
Kevin Knight and Ishwar Chander. 1994 · 1994
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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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A study of translation edit rate with targeted human annotation
Matthew Snover, Bonnie Dorr, Richard Shwartz, Linnea Micciulla, and John Makhoul. 2006 · 2006
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(meta-) evaluation of machine translation
Chris Callison-Burch, Cameron Fordyce, Philipp Koehn, Christof Monz, and Josh Schroeder. 2007 · 2007
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Statistical phrase-based post-editing
Michel Simard, Cyril Goutte, and Pierre Isabelle. 2007 · 2007
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The NIST 2008 Metrics for machine translation challenge — overview, methodology, metrics, and results
Mark Przybocki, Kay Peterson, Sébastien Bronsart, and Gregory Sanders. 2009 · 2008
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Statistical post-editing for a statistical MT system
Hanna Béchara, Yanjun Ma, and Josef van Genabith. 2011 · 2011
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langid.py: An off-the-shelf language identification tool
Marco Lui and Timothy Baldwin. 2012 · 2012
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Machine translation infrastructure and post-editing performance at Autodesk
Ventsislav Zhechev. 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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Manawi: Using multi-word expressions and named entities to improve machine translation
Liling Tan and Santanu Pal. 2014 · 2014
Cited alongside, same era.
Findings of the 2015 Workshop on Statistical Machine Translation
Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Barry Haddow, Matthias Huck, Chris Hokamp, Philipp Koehn, Varvara Logacheva, Christof Monz, Matteo Negri, Matt Post, Carolina Scarton, Lucia Specia, and Marco Turchi. 2015 · 2015
Cited alongside, same era.
Pronoun-focused mt and cross-lingual pronoun prediction: Findings of the 2015 discomt shared task on pronoun translation
Christian Hardmeier, Preslav Nakov, Sara Stymne, Jörg Tiedemann, Yannick Versley, and Mauro Cettolo. 2015 · 2015
Cited alongside, same era.
chrF: character n-gram F-score for automatic MT evaluation
Maja Popović. 2015 · 2015
Cited alongside, same era.
Findings of the 2016 conference on machine translation
Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Varvara Logacheva, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Martin Popel, Matt Post, Raphael Rubino, Carolina Scarton, Lucia Specia, Marco Turchi, Karin Verspoor, and Marcos Zampieri. 2016 · 2016
Findings of the WMT 2018 shared task on automatic post-editing
Rajen Chatterjee, Matteo Negri, Raphael Rubino, and Marco Turchi. 2018 · 2018
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A survey of domain adaptation for neural machine translation
Chenhui Chu and Rui Wang. 2018 · 2018
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MS-UEdin submission to the WMT2018 APE shared task: Dual-source transformer for automatic post-editing
Marcin Junczys-Dowmunt and Roman Grundkiewicz. 2018 · 2018
Later among the works it cites.
eSCAPE: a large-scale synthetic corpus for automatic post-editing
Matteo Negri, Marco Turchi, Rajen Chatterjee, and Nicola Bertoldi. 2018 · 2018
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A call for clarity in reporting BLEU scores
Matt Post. 2018 · 2018
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Multi-source transformer with combined losses for automatic post editing
Amirhossein Tebbifakhr, Ruchit Agrawal, Matteo Negri, and Marco Turchi. 2018 · 2018
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Cited alongside, same era.
Log-linear combinations of monolingual and bilingual neural machine translation models for automatic post-editing
Marcin Junczys-Dowmunt and Roman Grundkiewicz. 2016 · 2016
Cited alongside, same era.
OpenSubtitles2016: Extracting large parallel corpora from movie and TV subtitles
Pierre Lison and Jörg Tiedemann. 2016 · 2016
Cited alongside, same era.
Improving neural machine translation models with monolingual data
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
Cited alongside, same era.
Findings of the 2017 conference on machine translation (WMT17)
Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Shujian Huang, Matthias Huck, Philipp Koehn, Qun Liu, Varvara Logacheva, Christof Monz, Matteo Negri, Matt Post, Raphael Rubino, Lucia Specia, and Marco Turchi. 2017 · 2017
Cited alongside, same era.
Exploring the use of acoustic embeddings in neural machine translation
Salil Deena, Raymond WM Ng, Pranava Madhyastha, Lucia Specia, and Thomas Hain. 2017 · 2017
Cited alongside, same era.
The AMU-UEdin submission to the WMT 2017 shared task on automatic post-editing
Marcin Junczys-Dowmunt and Marcin Junczys-Dowmunt. 2017 · 2017
Cited alongside, same era.
Online neural automatic post-editing for neural machine translation
Matteo Negri, Marco Turchi, Nicola Bertoldi, and Marcello Federico. 2017 · 2017
Cited alongside, same era.
Later among the works it cites.
Probing the need for visual context in multimodal machine translation
Ozan Caglayan, Pranava Madhyastha, Lucia Specia, and Loïc Barrault. 2019 · 2019
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Findings of the WMT 2019 shared task on automatic post-editing
Rajen Chatterjee, Christian Federmann, Matteo Negri, and Marco Turchi. 2019 · 2019
Later among the works it cites.
A simple and effective approach to automatic post-editing with transfer learning
Gonçalo M. Correia and André F. T. Martins. 2019 · 2019
Later among the works it cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Later among the works it cites.
APE at scale and its implications on MT evaluation biases
Markus Freitag, Isaac Caswell, and Scott Roy. 2019 · 2019
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Unbabel’s submission to the WMT2019 APE shared task: BERT-based encoder-decoder for automatic post-editing
António V. Lopes, M. Amin Farajian, Gonçalo M. Correia, Jonay Trénous, and André F. T. Martins. 2019 · 2019
Later among the works it cites.
Context-aware monolingual repair for neural machine translation
Elena Voita, Rico Sennrich, and Ivan Titov. 2019 · 2019
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A post-editing dataset in the legal domain: Do we underestimate neural machine translation quality?
Julia Ive, Lucia Specia, Sara Szoc, Tom Vanallemeersch, Joachim Van den Bogaert, Eduardo Farah, Christine Maroti, Artur Ventura, and Maxim Khalilov. 2020 · 2020
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