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Hallucinations in machine translation are translations that contain information completely unrelated to the input.
Continuous measurement scales in human evaluation of machine translation
Yvette Graham, Timothy Baldwin, Alistair Moffat, and Justin Zobel. 2013 · 2013
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Multidimensional quality metrics (mqm): A framework for declaring and describing translation quality metrics
Arle Lommel, Aljoscha Burchardt, and Hans Uszkoreit. 2014 · 2014
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Visualizing and understanding convolutional networks
Matthew D. Zeiler and Rob Fergus. 2014 · 2014
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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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Representation of linguistic form and function in recurrent neural networks
Ákos Kádár, Grzegorz Chrupała, and Afra Alishahi. 2017 · 2017
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Understanding neural networks through representation erasure
Jiwei Li, Will Monroe, and Dan Jurafsky. 2017 · 2017
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chrF++: words helping character n-grams
Maja Popović. 2017 · 2017
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Evaluating neural network explanation methods using hybrid documents and morphosyntactic agreement
Nina Poerner, Hinrich Schütze, and Benjamin Roth. 2018 · 2018
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A call for clarity in reporting BLEU scores
Matt Post. 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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Attention is not Explanation
Sarthak Jain and Byron C. Wallace. 2019 · 2019
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Hallucinations in neural machine translation
Katherine Lee, Orhan Firat, Ashish Agarwal, Clara Fannjiang, and David Sussillo. 2019 · 2019
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On the word alignment from neural machine translation
Xintong Li, Guanlin Li, Lemao Liu, Max Meng, and Shuming Shi. 2019 · 2019
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Is attention interpretable?
Sofia Serrano and Noah A. Smith. 2019 · 2019
Cited alongside, same era.
On NMT search errors and model errors: Cat got your tongue?
Felix Stahlberg and Bill Byrne. 2019 · 2019
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Attention is not not explanation
Sarah Wiegreffe and Yuval Pinter. 2019 · 2019
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The elephant in the interpretability room: Why use attention as explanation when we have saliency methods?
Jasmijn Bastings and Katja Filippova. 2020 · 2020
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Attention is not only a weight: Analyzing transformers with vector norms
Goro Kobayashi, Tatsuki Kuribayashi, Sho Yokoi, and Kentaro Inui. 2020 · 2020
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Domain robustness in neural machine translation
Mathias Müller, Annette Rios, and Rico Sennrich. 2020 · 2020
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Optimal transport for unsupervised hallucination detection in neural machine translation
Nuno M. Guerreiro, Pierre Colombo, Pablo Piantanida, and André F. T. Martins. 2022 · 2022
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Bitext mining using distilled sentence representations for low-resource languages
Kevin Heffernan, Onur Çelebi, and Holger Schwenk. 2022 · 2022
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Consistent human evaluation of machine translation across language pairs
Daniel Licht, Cynthia Gao, Janice Lam, Francisco Guzman, Mona Diab, and Philipp Koehn. 2022 · 2022
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No language left behind: Scaling human-centered machine translation
NLLB Team, Marta R. Costa-jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula, Loic Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, John Hoffman, Semarley Jarrett, Kaushik Ram Sadagopan, Dirk Rowe, Shannon Spruit, Chau Tran, Pierre Andrews, Necip Fazil Ayan, Shruti Bhosale, Sergey Edunov, Angela Fan, Cynthia Gao, Vedanuj Goswami, Francisco Guzmán, Philipp Koehn, Alexandre Mourachko, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, and Jeff Wang. 2022 · 2022
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COMET: A neural framework for MT evaluation
Ricardo Rei, Craig Stewart, Ana C Farinha, and Alon Lavie. 2020 · 2020
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Measuring and increasing context usage in context-aware machine translation
Patrick Fernandes, Kayo Yin, Graham Neubig, and André F. T. Martins. 2021 · 2021
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Understanding the properties of minimum Bayes risk decoding in neural machine translation
Mathias Müller and Rico Sennrich. 2021 · 2021
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The curious case of hallucinations in neural machine translation
Vikas Raunak, Arul Menezes, and Marcin Junczys-Dowmunt. 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
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Language-agnostic BERT sentence embedding
Fangxiaoyu Feng, Yinfei Yang, Daniel Cer, Naveen Arivazhagan, and Wei Wang. 2022 · 2022
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As little as possible, as much as necessary: Detecting over- and undertranslations with contrastive conditioning
Jannis Vamvas and Rico Sennrich. 2022 · 2022
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David Dale, Elena Voita, Loïc Barrault, and Marta R. Costa-jussà. 2023 · 2023
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Looking for a needle in a haystack: A comprehensive study of hallucinations in neural machine translation
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Jigsaw multilingual toxic comment classification
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Understanding and detecting hallucinations in neural machine translation via model introspection
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Multilingual machine translation with large language models: Empirical results and analysis
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