This article reports on a study designed to quantify temporal and technical post-editing effort in machine translated texts. The study involved two groups of English>German translators, one instructed to post-edit a machine-translated text and the other to translate the same text from English into German. In addition, text segments containing negative translatability indicators (NTIs) were isolated in order to compare the effort involved in post-editing and translating segments with such indicators and without. Results indicate that the post-editing effort for all segments was lower than the translation effort for those segments. Additional results suggest, among other things, that the removal of NTIs may reduce relative post-editing effort overall.
2024. The impact of machine translation on the development of info-mining and thematic competences in legal translation trainees: a focus on time and external resources. The Interpreter and Translator Trainer 18:2 ► pp. 290 ff.
Alvarez-Vidal, Sergi & Antoni Oliver
2023. Assessing MT with measures of PE effort. Ampersand 11 ► pp. 100125 ff.
2019. Post-editing neural machine translation versus phrase-based machine translation for English–Chinese. Machine Translation 33:1-2 ► pp. 9 ff.
Munková, Daša, Oľga Wrede & Jakub Absolon
2019. Vergleich der menschlichen, maschinellen und Post-Editing-Übersetzung aus dem Slowakischen ins Deutsche mittels automatischer Evaluation. Zeitschrift für Slawistik 64:2 ► pp. 231 ff.
Sun, Sanjun
2019. Measuring Difficulty in Translation and Post-editing: A Review. In Researching Cognitive Processes of Translation [New Frontiers in Translation Studies, ], ► pp. 139 ff.
마승혜
2018. Expanding the Scope of Machine Translation-Machine Translation Post Editing Strategies for Specific Metaphors in Newspapers. The Journal of Translation Studies 19:2 ► pp. 117 ff.
Mellinger, Christopher D.
2017. Translators and machine translation: knowledge and skills gaps in translator pedagogy. The Interpreter and Translator Trainer 11:4 ► pp. 280 ff.
2017. Productivity and quality when editing machine translation and translation memory outputs: an empirical analysis of English to Welsh translation. Studia Celtica Posnaniensia 2:1 ► pp. 1 ff.
Alves, Fabio, Arlene Koglin, Bartolomé Mesa-Lao, Mercedes García Martínez, Norma B. de Lima Fonseca, Arthur de Melo Sá, José Luiz Gonçalves, Karina Sarto Szpak, Kyoko Sekino & Marceli Aquino
2016. Analysing the Impact of Interactive Machine Translation on Post-editing Effort. In New Directions in Empirical Translation Process Research [New Frontiers in Translation Studies, ], ► pp. 77 ff.
Yamada, Masaru
2015. Can college students be post-editors? An investigation into employing language learners in machine translation plus post-editing settings. Machine Translation 29:1 ► pp. 49 ff.
Pym, Anthony
2014. Translation Skill-Sets in a Machine-Translation Age. Meta 58:3 ► pp. 487 ff.
Koby, Geoffrey S.
2012. Post‐Editing of Machine Translation. In The Encyclopedia of Applied Linguistics,
2011. Translation practice in the workplace: contextual analysis and implications for machine translation. Machine Translation 25:1 ► pp. 35 ff.
O’Brien, Sharon
2011. Towards predicting post-editing productivity. Machine Translation 25:3 ► pp. 197 ff.
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