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Pushing the right buttons: adversarial evaluation of quality estimation
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In: Proceedings of the Sixth Conference on Machine Translation ; 625 ; 638 (2022)
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Reverse racism: the construction of a slip narrative ; Racismo inverso: la construcción de una narrativa deslizante ; Racismo reverso: a construção de uma narrativa de esquiva
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In: Signótica; Vol. 34 (2022) ; Signótica; v. 34 (2022) ; 2316-3690 ; 0103-7250 (2022)
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When Does Translation Require Context? A Data-driven, Multilingual Exploration ...
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Measuring and Increasing Context Usage in Context-Aware Machine Translation ...
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Do Context-Aware Translation Models Pay the Right Attention? ...
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Findings of the WMT 2021 Shared Task on Quality Estimation ...
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Do Context-Aware Translation Models Pay the Right Attention? ...
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Findings of the WMT 2021 shared task on quality estimation
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In: 689 ; 730 (2021)
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MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset ...
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Understanding the Mechanics of SPIGOT: Surrogate Gradients for Latent Structure Learning ...
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Findings of the WMT 2020 shared task on quality estimation
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In: 743 ; 764 (2020)
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Abstract:
© 2020 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://www.aclweb.org/anthology/2020.wmt-1.79 ; We report the results of the WMT20 shared task on Quality Estimation, where the challenge is to predict the quality of the output of neural machine translation systems at the word, sentence and document levels. This edition included new data with open domain texts, direct assessment annotations, and multiple language pairs: English-German, English-Chinese, Russian-English, Romanian-English, Estonian-English, Sinhala-English and Nepali-English data for the sentence-level subtasks, English-German and English-Chinese for the word-level subtask, and English-French data for the document-level subtask. In addition, we made neural machine translation models available to participants. 19 participating teams from 27 institutions submitted altogether 1374 systems to different task variants and language pairs.
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URL: http://hdl.handle.net/2436/623855
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MLQE-PE: A multilingual quality estimation and post-editing dataset
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