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1
Source or target first? Comparison of two post-editing strategies with translation students
In: https://hal.archives-ouvertes.fr/hal-03546151 ; 2022 (2022)
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2
Neural MT and Human Post-editing : a Method to Improve Editorial Quality
In: ISSN: 1134-8941 ; Interlingüística ; https://halshs.archives-ouvertes.fr/halshs-03603590 ; Interlingüística, Alacant [Spain] : Universitat Autònoma de Barcelona, 2022, pp.15-36 (2022)
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3
The use of MT by undergraduate translation students for different learning tasks
In: https://hal.archives-ouvertes.fr/hal-03547415 ; 2022 (2022)
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4
THE BENEFITS OF PEER EDITING IN THE CLASSROOM ...
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THE BENEFITS OF PEER EDITING IN THE CLASSROOM ...
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6
Early Modern German Shakespeare : "Titus Andronicus" and "The Taming of the Shrew" : "Tito Andronico" and "Kunst über alle Künste, ein bös Weib gut zu machen" in Translation
Erne, Lukas Christian; Hazrat, Florence; Shmygol, Maria. - : Bloomsbury (London), 2022
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7
Таксис в структуре текста (на материале рассказа Б. Пастернака «Письма из Тулы») ; Taxis in Text Structure (Based on B. Pasternak’s Letters from Tula)
Грудева, Е. В.; Соловьева, С. А.; Grudeva, E. V.. - : Уральский федеральный университет, 2022
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8
Monte Carlo modelling of confidence intervals in translation quality evaluation (TQE) and post-editing dstance (PED) measurement
In: Alekseeva, Alexandra orcid:0000-0002-7990-4592 , Gladkoff, Serge, Sorokina, Irina and Han, Lifeng orcid:0000-0002-3221-2185 (2021) Monte Carlo modelling of confidence intervals in translation quality evaluation (TQE) and post-editing dstance (PED) measurement. In: Metrics 2021: Workshop on Informetric and Scientometric Research (SIG-MET), 23-24 Oct 2021, Online. (2021)
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9
Exploring ideological messages in newspaper editorials and news reports on the first human gene-editing case ...
Nikitina, Jekaterina. - : University of Salento, 2021
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10
LeXmart ; A platform designed with lexicographical data in mind
Simões, Alberto; Salgado, Ana de Castro; Costa, Rute. - : Lexical Computing CZ s.r.o., 2021
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11
With, Without, Even Still: Frederick Douglass, L’Union, and Editorship Studies ...
Casey, Jim; Salter, Sarah H.. - : Humanities Commons, 2021
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12
Dictionarium Valachico-Latinum, the first original bilingual lexicon in Romanian
Gheorghe Chivu. - : Diacronia, 2021
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13
Comments upon the sources of the Church Slavonic–Ruthenian Lexicon by Pamvo Berynda
Alexander Levichkin. - : Diacronia, 2021
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14
The beginnings of Polish lexicography: How to present glosses in the medieval dictionary in the digital age?
Katarzyna Jasińska. - : Diacronia, 2021
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15
Typographical revision and language option in the Evangelium from Snagov, 1697
Adina Chirilă; Roxana Vieru. - : Diacronia, 2021
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16
Lexicography and the history of culture (The case of Teodor Corbea’s Romanian–Latin Dictionary)
Alin-Mihai Gherman. - : Diacronia, 2021
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17
Romanian Slavonic in Unicode: problems and solutions
Ion-Mihai Felea. - : Diacronia, 2021
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18
Slavonic–Romanian lexicons of the 17th century and their comparative digital edition (the eRomLex project)
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19
How Agricultural Digital Innovation Can Benefit from Semantics: The Case of the AGROVOC Multilingual Thesaurus
In: Engineering Proceedings ; Volume 9 ; Issue 1 (2021)
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20
The Role of Machine Translation Quality Estimation in the Post-Editing Workflow
In: Informatics ; Volume 8 ; Issue 3 (2021)
Abstract: As Machine Translation (MT) becomes increasingly ubiquitous, so does its use in professional translation workflows. However, its proliferation in the translation industry has brought about new challenges in the field of Post-Editing (PE). We are now faced with a need to find effective tools to assess the quality of MT systems to avoid underpayments and mistrust by professional translators. In this scenario, one promising field of study is MT Quality Estimation (MTQE), as this aims to determine the quality of an automatic translation and, indirectly, its degree of post-editing difficulty. However, its impact on the translation workflows and the translators’ cognitive load is still to be fully explored. We report on the results of an impact study engaging professional translators in PE tasks using MTQE. To assess the translators’ cognitive load we measure their productivity both in terms of time and effort (keystrokes) in three different scenarios: translating from scratch, post-editing without using MTQE, and post-editing using MTQE. Our results show that good MTQE information can improve post-editing efficiency and decrease the cognitive load on translators. This is especially true for cases with low MT quality.
Keyword: machine translation quality estimation; post-editing
URL: https://doi.org/10.3390/informatics8030061
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