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Findings of the WMT 2020 Shared Task on Machine Translation Robustness.

, , , , , , , , , , , и . WMT@EMNLP, стр. 76-91. Association for Computational Linguistics, (2020)

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Lucia Specia

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WMDO: Fluency-based Word Mover's Distance for Machine Translation Evaluation., , и . WMT (2), стр. 494-500. Association for Computational Linguistics, (2019)Deciding When, How and for Whom to Simplify., , и . ECAI, том 325 из Frontiers in Artificial Intelligence and Applications, стр. 2172-2179. IOS Press, (2020)Sentence-level confidence estimation for MT., , , , , , , и . SMART@EAMT, European Association for Machine Translation, (2009)Pangeanic in the EXPERT Project: Exploiting Empirical appRoaches to Translation., , , , и . MTSummit (European projects), (2013)Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications., , , , , и . EMNLP (1), стр. 5865-5875. Association for Computational Linguistics, (2021)EASSE: Easier Automatic Sentence Simplification Evaluation., , , и . EMNLP/IJCNLP (3), стр. 49-54. Association for Computational Linguistics, (2019)A Post-Editing Dataset in the Legal Domain: Do we Underestimate Neural Machine Translation Quality?, , , , , , , , и . LREC, стр. 3692-3697. European Language Resources Association, (2020)ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations., , , , , и . ACL, стр. 4668-4679. Association for Computational Linguistics, (2020)MoESD: Mixture of Experts Stable Diffusion to Mitigate Gender Bias., и . CoRR, (2024)FIND: Human-in-the-Loop Debugging Deep Text Classifiers., , и . EMNLP (1), стр. 332-348. Association for Computational Linguistics, (2020)