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In this paper, we describe compare-mt, a tool for holistic analysis and comparison of the results of systems for language generation tasks such as machine translation.
Error classification for mt evaluation
Mary Flanagan. 1994 · 1994
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Inderjeet Mani. 1999 · 1999
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Stochastic language generation for spoken dialogue systems
Alice H Oh and Alexander I Rudnicky. 2000 · 2000
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Ehud Reiter and Robert Dale. 2000 · 2000
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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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Phillip Koehn, Franz Josef Och, and Daniel Marcu. 2003 · 2003
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Statistical significance tests for machine translation evaluation
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Steve DeNeefe, Kevin Knight, and Hayward H. Chan. 2005 · 2005
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Adam Lopez and Philip Resnik. 2005 · 2005
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Masaki Murata, Kiyotaka Uchimoto, Qing Ma, Toshiyuki Kanamaru, and Hitoshi Isahara. 2005 · 2005
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Morpho-syntactic information for automatic error analysis of statistical machine translation output
Maja Popović, Adrià de Gispert, Deepa Gupta, Patrik Lambert, Hermann Ney, José B. Mariño, Marcello Federico, and Rafael Banchs. 2006 · 2006
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Error analysis of statistical machine translation output
David Vilar, Jia Xu, Luis Fernando d’Haro, and Hermann Ney. 2006 · 2006
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Word error rates: Decomposition over POS classes and applications for error analysis
Maja Popović and Hermann Ney. 2007 · 2007
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Diagnostic evaluation of machine translation systems using automatically constructed linguistic check-points
Ming Zhou, Bo Wang, Shujie Liu, Mu Li, Dongdong Zhang, and Tiejun Zhao. 2008 · 2008
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Metrics for mt evaluation: evaluating reordering
Alexandra Birch, Miles Osborne, and Phil Blunsom. 2010 · 2010
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Automatic evaluation of translation quality for distant language pairs
Hideki Isozaki, Tsutomu Hirao, Kevin Duh, Katsuhito Sudoh, and Hajime Tsukada. 2010 · 2010
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Philipp Koehn. 2010 · 2010
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Michael Denkowski and Alon Lavie. 2011 · 2011
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Ahmed El Kholy and Nizar Habash. 2011 · 2011
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Neural machine translation by jointly learning to align and translate
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Mt-compareval: Graphical evaluation interface for machine translation development
Ondřej Klejch, Eleftherios Avramidis, Aljoscha Burchardt, and Martin Popel. 2015 · 2015
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chrf: character n-gram f-score for automatic mt evaluation
Maja Popović. 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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How translation alters sentiment
Saif M Mohammad, Mohammad Salameh, and Svetlana Kiritchenko. 2016 · 2016
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Visualizing and understanding neural machine translation
Yanzhuo Ding, Yang Liu, Huanbo Luan, and Maosong Sun. 2017 · 2017
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A framework for diagnostic evaluation of mt based on linguistic checkpoints
Sudip Kumar Naskar, Antonio Toral, Federico Gaspari, and Andy Way. 2011 · 2011
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Towards automatic error analysis of machine translation output
Maja Popović and Hermann Ney. 2011 · 2011
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BLAST: A tool for error analysis of machine translation output
Sara Stymne. 2011 · 2011
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Addicter: What is wrong with my translations?
Daniel Zeman, Mark Fishel, Jan Berka, and Ondřej Bojar. 2011 · 2011
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Pet: a tool for post-editing and assessing machine translation
Wilker Aziz, Sheila Castilho, and Lucia Specia. 2012 · 2012
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Appraise: an open-source toolkit for manual evaluation of mt output
Christian Federmann. 2012 · 2012
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A challenge set approach to evaluating machine translation
Pierre Isabelle, Colin Cherry, and George Foster. 2017 · 2017
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Interactive visualization and manipulation of attention-based neural machine translation
Jaesong Lee, Joong-Hwi Shin, and Jun-Seok Kim. 2017 · 2017
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How Grammatical is Character-level Neural Machine Translation? Assessing MT Quality with Contrastive Translation Pairs
Rico Sennrich. 2017 · 2017
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Evaluating Discourse Phenomena in Neural Machine Translation
Rachel Bawden, Rico Sennrich, Alexandra Birch, and Barry Haddow. 2018 · 2018
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MTNT: A testbed for machine translation of noisy text
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A large-scale test set for the evaluation of context-aware pronoun translation in neural machine translation
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Rapid adaptation of neural machine translation to new languages
Graham Neubig and Junjie Hu. 2018 · 2018
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When and why are pre-trained word embeddings useful for neural machine translation?
Ye Qi, Devendra Sachan, Matthieu Felix, Sarguna Padmanabhan, and Graham Neubig. 2018 · 2018
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Parameter sharing methods for multilingual self-attentional translation models
Devendra Sachan and Graham Neubig. 2018 · 2018
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A tree-based decoder for neural machine translation
Xinyi Wang, Hieu Pham, Pengcheng Yin, and Graham Neubig. 2018 · 2018
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Von mises-fisher loss for training sequence to sequence models with continuous outputs
Sachin Kumar and Yulia Tsvetkov. 2019 · 2019
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