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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 Hits 1 – 20 of 29 1 Towards Explainable Evaluation Metrics for Natural Language Generation ... Leiter, Christoph; Lertvittayakumjorn, Piyawat; Fomicheva, Marina. - : arXiv, 2022 BASE Show details 2 Pushing the right buttons: adversarial evaluation of quality estimation Specia, Lucia; Fomicheva, Marina; Ranasinghe, Tharindu; Orasan, Constantin; Kanojia, Diptesh; Blain, Frederic In: Proceedings of the Sixth Conference on Machine Translation ; 625 ; 638 (2022) Abstract: © (2021) The Authors. Published by Association for Computational Linguistics. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://aclanthology.org/2021.wmt-1.67 ; Current Machine Translation (MT) systems achieve very good results on a growing variety of language pairs and datasets. However, they are known to produce fluent translation outputs that can contain important meaning errors, thus undermining their reliability in practice. Quality Estimation (QE) is the task of automatically assessing the performance of MT systems at test time. Thus, in order to be useful, QE systems should be able to detect such errors. However, this ability is yet to be tested in the current evaluation practices, where QE systems are assessed only in terms of their correlation with human judgements. In this work, we bridge this gap by proposing a general methodology for adversarial testing of QE for MT. First, we show that despite a high correlation with human judgements achieved by the recent SOTA, certain types of meaning errors are still problematic for QE to detect. Second, we show that on average, the ability of a given model to discriminate between meaningpreserving and meaning-altering perturbations is predictive of its overall performance, thus potentially allowing for comparing QE systems without relying on manual quality annotation. Keyword: adversarial evaluation; machine translation; quality estimation URL: http://hdl.handle.net/2436/624376 BASE Hide details 3 Translation Error Detection as Rationale Extraction ... Fomicheva, Marina; Specia, Lucia; Aletras, Nikolaos. - : arXiv, 2021 BASE Show details 4 Knowledge Distillation for Quality Estimation ... Gajbhiye, Amit; Fomicheva, Marina; Alva-Manchego, Fernando. - : arXiv, 2021 BASE Show details 5 Continual Quality Estimation with Online Bayesian Meta-Learning ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Fomicheva, Marina; Obamuyide, Abiola. - : Underline Science Inc., 2021 BASE Show details 6 Knowledge Distillation for Quality Estimation ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Aletras, Nikolaos; Alva-Manchego, Fernando. - : Underline Science Inc., 2021 BASE Show details 7 Findings of the WMT 2021 Shared Task on Quality Estimation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Blain, Frédéric; Chaudhary, Vishrav. - : Underline Science Inc., 2021 BASE Show details 8 Pushing the Right Buttons: Adversarial Evaluation of Quality Estimation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Tharindu; Blain, Frédéric. - : Underline Science Inc., 2021 BASE Show details 9 Knowledge distillation for quality estimation Gajbhiye, Amit; Fomicheva, Marina; Alva-Manchego, Fernando. - : Association for Computational Linguistics, 2021 BASE Show details 10 deepQuest-py: large and distilled models for quality estimation Alva-Manchego, Fernando; Obamuyide, Abiola; Gajbhiye, Amit. - : Association for Computational Linguistics, 2021 BASE Show details 11 Findings of the WMT 2021 shared task on quality estimation Specia, Lucia; Chaudhary, Vishrav; Martins, André... In: 689 ; 730 (2021) BASE Show details 12 deepQuest-py: large and distilled models for quality estimation Gajbhiye, Amit; Specia, Lucia; Obamuyide, Abiola... In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations ; 382 ; 389 (2021) BASE Show details 13 Backtranslation feedback improves user confidence in MT, not quality Obregón, Mateo; Fomicheva, Marina; Novák, Michal. - : Association for Computational Linguistics, 2021 BASE Show details 14 Knowledge distillation for quality estimation Specia, Lucia; Aletras, Nikolaos; Gajbhiye, Amit... In: 5091 ; 5099 (2021) BASE Show details 15 MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset ... Fomicheva, Marina; Sun, Shuo; Fonseca, Erick. - : arXiv, 2020 BASE Show details 16 Unsupervised quality estimation for neural machine translation Fomicheva, Marina; Guzmán, Francisco; Chaudhary, Vishrav... In: 8 ; 539 ; 555 (2020) BASE Show details 17 An exploratory study on multilingual quality estimation Renduchintala, Adithya; Fomicheva, Marina; Blain, Frederic... In: 366 ; 377 (2020) BASE Show details 18 BERGAMOT-LATTE submissions for the WMT20 quality estimation shared task Guzmán, Francisco; Fomicheva, Marina; Blain, Frédéric... In: 1010 ; 1017 (2020) BASE Show details 19 Findings of the WMT 2020 shared task on quality estimation Fomicheva, Marina; Fonseca, Erick; Guzmán, Francisco... In: 743 ; 764 (2020) BASE Show details 20 MLQE-PE: A multilingual quality estimation and post-editing dataset Fomicheva, Marina; Sun, Shuo; Fonseca, Erick. - : arXiv, 2020 BASE Show details Page: 1 2

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