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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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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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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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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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Neuronale maschinelle Übersetzung für ressourcenarme Szenarien ... : Neural machine translation for low-resource scenarios ...
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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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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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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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Abstract:
Neural machine translation systems have revolutionized translation processes in terms of quantity and speed in recent years, and they have even been claimed to achieve human parity. However, the quality of their output has also raised serious doubts and concerns, such as loss in lexical variation, evidence of “machine translationese”, and its effect on post-editing, which results in “post-editese”. In this study, we analyze the outputs of three English to Slovenian machine translation systems in terms of lexical diversity in three different genres. Using both quantitative and qualitative methods, we analyze one statistical and two neural systems, and we compare them to a human reference translation. Our quantitative analyses based on lexical diversity metrics show diverging results; however, translation systems, particularly neural ones, mostly exhibit larger lexical diversity than their human counterparts. Nevertheless, a qualitative method shows that these quantitative results are not always a reliable tool to assess true lexical diversity and that a lot of lexical “creativity”, especially by neural translation systems, is often unreliable, inconsistent, and misguided.
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Keyword:
lexical diversity; machine translation; measure of textual lexical diversity; neural translation systems; type-token ratio
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URL: https://doi.org/10.3390/info13020093
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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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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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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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Sign Language Avatars: A Question of Representation
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In: Information; Volume 13; Issue 4; Pages: 206 (2022)
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Identifying Source-Language Dialects in Translation
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In: Mathematics; Volume 10; Issue 9; Pages: 1431 (2022)
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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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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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Hebrew Transformed: Machine Translation of Hebrew Using the Transformer Architecture
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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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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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