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Neural machine translation (NMT) has become the de-facto standard in real-world machine translation applications.
Europarl: A parallel corpus for statistical machine translation
Philipp Koehn. 2005 · 2005
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On the translocation of masses
Leonid V Kantorovich. 2006 · 2006
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Optimal transport: old and new , volume 338
Cédric Villani. 2009 · 2009
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011 · 2011
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
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chrF deconstructed: beta parameters and n-gram weights
Maja Popović. 2016 · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Findings of the 2018 conference on machine translation (WMT18)
Ondřej Bojar, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Philipp Koehn, and Christof Monz. 2018 · 2018
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Hallucinations in neural machine translation
Katherine Lee, Orhan Firat, Ashish Agarwal, Clara Fannjiang, and David Sussillo. 2018 · 2018
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Computational optimal transport
Gabriel Peyré and Marco Cuturi. 2018 · 2018
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An analysis of encoder representations in transformer-based machine translation
Alessandro Raganato and Jörg Tiedemann. 2018 · 2018
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Naver labs Europe’s systems for the WMT19 machine translation robustness task
Alexandre Berard, Ioan Calapodescu, and Claude Roux. 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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Findings of the WMT 2019 shared task on parallel corpus filtering for low-resource conditions
Philipp Koehn, Francisco Guzmán, Vishrav Chaudhary, and Juan Pino. 2019 · 2019
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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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Subspace robust wasserstein distances
François-Pierre Paty and Marco Cuturi. 2019 · 2019
Cited alongside, same era.
Computational optimal transport: With applications to data science
Gabriel Peyré, Marco Cuturi, et al. 2019 · 2019
Cited alongside, same era.
Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
Cited alongside, same era.
Language-agnostic bert sentence embedding
Fangxiaoyu Feng, Yinfei Yang, Daniel Cer, Naveen Arivazhagan, and Wei Wang. 2020 · 2020
Cited alongside, same era.
Attention is not only a weight: Analyzing transformers with vector norms
Goro Kobayashi, Tatsuki Kuribayashi, Sho Yokoi, and Kentaro Inui. 2020 · 2020
Cited alongside, same era.
Domain robustness in neural machine translation
Mathias Müller, Annette Rios, and Rico Sennrich. 2020 · 2020
2d wasserstein loss for robust facial landmark detection
Yongzhe Yan, Stefan Duffner, Priyanka Phutane, Anthony Berthelier, Christophe Blanc, Christophe Garcia, and Thierry Chateau. 2021 · 2021
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Detecting hallucinated content in conditional neural sequence generation
Chunting Zhou, Graham Neubig, Jiatao Gu, Mona Diab, Francisco Guzmán, Luke Zettlemoyer, and Marjan Ghazvininejad. 2021 · 2021
Later among the works it cites.
Hyperspectral anomaly detection based on wasserstein distance and spatial filtering
Xiaoyu Cheng, Maoxing Wen, Cong Gao, and Yueming Wang. 2022 · 2022
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David Dale, Elena Voita, Loïc Barrault, and Marta R. Costa-jussà. 2022 · 2022
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Towards opening the black box of neural machine translation: Source and target interpretations of the transformer
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Cited alongside, same era.
COMET: A neural framework for MT evaluation
Ricardo Rei, Craig Stewart, Ana C Farinha, and Alon Lavie. 2020 · 2020
Cited alongside, same era.
On exposure bias, hallucination and domain shift in neural machine translation
Chaojun Wang and Rico Sennrich. 2020 · 2020
Cited alongside, same era.
Attention weights in transformer NMT fail aligning words between sequences but largely explain model predictions
Javier Ferrando and Marta R. Costa-jussà. 2021 · 2021
Cited alongside, same era.
Understanding the properties of minimum Bayes risk decoding in neural machine translation
Mathias Müller and Rico Sennrich. 2021 · 2021
Cited alongside, same era.
The curious case of hallucinations in neural machine translation
Vikas Raunak, Arul Menezes, and Marcin Junczys-Dowmunt. 2021 · 2021
Cited alongside, same era.
Findings of the WMT 2021 shared task on quality estimation
Lucia Specia, Frédéric Blain, Marina Fomicheva, Chrysoula Zerva, Zhenhao Li, Vishrav Chaudhary, and André F. T. Martins. 2021 · 2021
Cited alongside, same era.
Javier Ferrando, Gerard I. Gállego, Belen Alastruey, Carlos Escolano, and Marta R. Costa-jussà. 2022 · 2022
Closest in time.
MLQE-PE: A multilingual quality estimation and post-editing dataset
Marina Fomicheva, Shuo Sun, Erick Fonseca, Chrysoula Zerva, Frédéric Blain, Vishrav Chaudhary, Francisco Guzmán, Nina Lopatina, Lucia Specia, and André F. T. Martins. 2022 · 2022
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Looking for a needle in a haystack: A comprehensive study of hallucinations in neural machine translation
Nuno M. Guerreiro, Elena Voita, and André F. T. Martins. 2022 · 2022
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Findings of the 2022 conference on machine translation (wmt22)
Tom Kocmi, Rachel Bawden, OndÅ™ej Bojar, Anton Dvorkovich, Christian Federmann, Mark Fishel, Thamme Gowda, Yvette Graham, Roman Grundkiewicz, Barry Haddow, Rebecca Knowles, Philipp Koehn, Christof Monz, Makoto Morishita, Masaaki Nagata, Toshiaki Nakazawa, Michal Novák, Martin Popel, Maja Popović, and Mariya Shmatova. 2022 · 2022
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Red teaming language models with language models
Ethan Perez, Saffron Huang, Francis Song, Trevor Cai, Roman Ring, John Aslanides, Amelia Glaese, Nat McAleese, and Geoffrey Irving. 2022 · 2022
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Salted: A framework for salient long-tail translation error detection
Vikas Raunak, Matt Post, and Arul Menezes. 2022 · 2022
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Cometkiwi: Ist-unbabel 2022 submission for the quality estimation shared task
Ricardo Rei, Marcos Treviso, Nuno M. Guerreiro, Chrysoula Zerva, Ana C. Farinha, Christine Maroti, José G. C. de Souza, Taisiya Glushkova, Duarte M. Alves, Alon Lavie, Luisa Coheur, and André F. T. Martins. 2022 · 2022
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Reducing hallucinations in neural machine translation with feature attribution
Joël Tang, Marina Fomicheva, and Lucia Specia. 2022 · 2022
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Findings of the WMT 2022 shared task on quality estimation
Chrysoula Zerva, Frédéric Blain, Ricardo Rei, Piyawat Lertvittayakumjorn, José G. C. de Souza, Steffen Eger, Diptesh Kanojia, Duarte Alves, Constantin Orǎsan, Marina Fomicheva, André F. T. Martins, and Lucia Specia. 2022 · 2022
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Looking for a needle in a haystack: A comprehensive study of hallucinations in neural machine translation
Nuno M. Guerreiro, Elena Voita, and André Martins. 2023 · 2023
Closest in time.