SLIDE: Reference-free Evaluation for Machine Translation using a Sliding Document Window

Cross-lingual language model pretraining

Original

Guillaume Lample and Alexis Conneau. 2019 · 1901

Earlier work this paper cites.

A survey on document-level machine translation: Methods and evaluation

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Sameen Maruf, Fahimeh Saleh, and Gholamreza Haffari. 2019 · 1912

Earlier work this paper cites.

Evaluating discourse phenomena in neural machine translation

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Earlier work this paper cites.

A large-scale test set for the evaluation of context-aware pronoun translation in neural machine translation

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Earlier work this paper cites.

When a good translation is wrong in context: Context-aware machine translation improves on deixis, ellipsis, and lexical cohesion

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Elena Voita, Rico Sennrich, and Ivan Titov. 2019 · 2019

Earlier work this paper cites.

Document-level neural MT: A systematic comparison

António Lopes, M. Amin Farajian, Rachel Bawden, Michael Zhang, and André F. T. Martins. 2020 · 2020

Earlier work this paper cites.

Unbabel’s participation in the WMT20 metrics shared task

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InfoXLM: An information-theoretic framework for cross-lingual language model pre-training

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Larger-scale transformers for multilingual masked language modeling

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To ship or not to ship: An extensive evaluation of automatic metrics for machine translation

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Results of WMT22 metrics shared task: Stop using BLEU – neural metrics are better and more robust

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Findings of the 2022 conference on machine translation (WMT22)

Tom Kocmi, Rachel Bawden, Ondřej 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, and Maja Popović. 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 Alves, Luisa Coheur, Alon Lavie, and André F. T. Martins. 2022 · 2022

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Embarrassingly easy document-level MT metrics: How to convert any pretrained metric into a document-level metric

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Training and meta-evaluating machine translation evaluation metrics at the paragraph level

Original

Daniel Deutsch, Juraj Juraska, Mara Finkelstein, and Markus Freitag. 2023 · 2023

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