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Source or target first? Comparison of two post-editing strategies with translation students
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In: https://hal.archives-ouvertes.fr/hal-03546151 ; 2022 (2022)
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| 2 |
Thirty Years of Machine Translation in Language Teaching and Learning: A Review of the Literature
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In: L2 Journal, vol 14, iss 1 (2022)
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| 3 |
Do You Speak Translate?: Reflections on the Nature and Role of Translation
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In: L2 Journal, vol 14, iss 1 (2022)
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| 4 |
АКТУАЛЬНЫЕ ТЕНДЕНЦИИ ЦИФРОВИЗАЦИИ ИНОЯЗЫЧНОГО ОБУЧЕНИЯ В НЕЯЗЫКОВОМ ВУЗЕ ... : CURRENT TRENDS IN DIGITALIZATION OF FOREIGN LANGUAGE EDUCATION IN A NON-LINGUISTIC UNIVERSITY ...
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| 5 |
Neuronale maschinelle Übersetzung für ressourcenarme Szenarien ... : Neural machine translation for low-resource scenarios ...
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| 6 |
MCSQ Translation Models (en-ru) (v1.0)
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Variš, Dušan. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2022
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| 7 |
MCSQ Translation Models (en-de) (v1.0)
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Variš, Dušan. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2022
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| 8 |
Machine Translation Testsuite for Gender-Consistent Translation
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Aires, João Paulo. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2022
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| 10 |
Lexical Diversity in Statistical and Neural Machine Translation
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In: Information; Volume 13; Issue 2; Pages: 93 (2022)
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| 11 |
Neural Models for Measuring Confidence on Interactive Machine Translation Systems
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In: Applied Sciences; Volume 12; Issue 3; Pages: 1100 (2022)
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| 12 |
Impact of Sentence Representation Matching in Neural Machine Translation
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In: Applied Sciences; Volume 12; Issue 3; Pages: 1313 (2022)
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| 13 |
Linguistic Mathematical Relationships Saved or Lost in Translating Texts: Extension of the Statistical Theory of Translation and Its Application to the New Testament
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In: Information; Volume 13; Issue 1; Pages: 20 (2022)
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| 14 |
Sign Language Avatars: A Question of Representation
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In: Information; Volume 13; Issue 4; Pages: 206 (2022)
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| 15 |
Identifying Source-Language Dialects in Translation
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In: Mathematics; Volume 10; Issue 9; Pages: 1431 (2022)
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| 16 |
Leveraging Frozen Pretrained Written Language Models for Neural Sign Language Translation
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In: Information; Volume 13; Issue 5; Pages: 220 (2022)
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| 17 |
X-Transformer: A Machine Translation Model Enhanced by the Self-Attention Mechanism
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In: Applied Sciences; Volume 12; Issue 9; Pages: 4502 (2022)
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| 18 |
Hebrew Transformed: Machine Translation of Hebrew Using the Transformer Architecture
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| 19 |
Technology in audiovisual translation practices and training ; Las tecnologías en la formación y las prácticas de traducción audiovisual
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In: CLINA Revista Interdisciplinaria de Traducción Interpretación y Comunicación Intercultural; Vol. 7 Núm. 1 (2021); 17-24 ; CLINA Revista Interdisciplinaria de Traducción Interpretación y Comunicación Intercultural; Vol. 7 No. 1 (2021); 17-24 ; 2444-1961 ; 10.14201/clina202171 (2022)
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| 20 |
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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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.
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Keyword:
adversarial evaluation; machine translation; quality estimation
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URL: http://hdl.handle.net/2436/624376
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