Cover not available
Article published In:
Translation Spaces
Vol. 7:2 (2018) ► pp.240–262

Translators’ perceptions of literary post-editing using statistical and neural machine translation

ORCID logo | Dublin City University
ORCID logo | University of Groningen
ORCID logo | Dublin City University
ORCID logo | Dublin City University
In the context of recent improvements in the quality of machine translation (MT) output and new use cases being found for that output, this article reports on an experiment using statistical and neural MT systems to translate literature. Six professional translators with experience of literary translation produced English-to-Catalan translations under three conditions: translation from scratch, neural MT post-editing, and statistical MT post-editing. They provided feedback before and after the translation via questionnaires and interviews. While all participants prefer to translate from scratch, mostly due to the freedom to be creative without the constraints of segment-level segmentation, those with less experience find the MT suggestions useful.
Keywords: machine translation, literary machine translation, neural machine translation, human factors in machine translation
Article outline
  • Introduction
  • Related literature
    • Perceptions of post-editing
    • Neural MT
    • MT for literature
  • Methodology
    • MT systems and training
    • Test data: Warbreaker
    • Participant profiles
    • Experimental setup
  • Results
    • Measurements and perceptions of post-editing effort
      • Temporal effort
      • Technical effort
    • Users’ perceptions of the task
      • Creativity vs. productivity
      • Segmentation and context
      • Source vs. target (adequacy vs. fluency)
      • SMT vs. NMT
      • Perception vs. reality
      • Alternative methods of machine assistance
  • Conclusion
  • Acknowledgements
  • Notes
  • References
Published online: 28 November 2018
DOI logo
https://doi.org/10.1075/ts.18014.moo
References (70)
References
Aziz, Wilker, Sheila Castilho M. de Sousa, and Lucia Specia. 2012. “PET: a tool for post-editing and assessing machine translation.” In The Eighth International Conference on Language Resources and Evaluation, LREC ‘12, Istanbul, Turkey. May 2012. [URL]
Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural Machine Translation by Jointly Learning to Align and Translate. Conference paper presented at the ICLR 2015. arXiv preprint, arXiv:1409.0473.
Bar-Hillel, Yehoshua. 1960. “The Present Status of Automatic Translation of Languages.” Advances in Computers 11: 91–163. DOI logoGoogle Scholar
Bentivogli, Luisa, Arianna Bisazza, Mauro Cettolo, Marcello Federico. 2017. “Neural versus Phrase-Based MT Quality: An In-Depth Analysis on English-German and English-French”. Computer Speech & Language 491: 52–70. DOI logoGoogle Scholar
Besacier, Laurent. 2014. “Traduction automatisée d’une œuvre littéraire: une étude pilote.” In Traitement Automatique du Langage Naturel (TALN). Marseille, France.Google Scholar
Bird, Steven. 2006. “NLTK: The Natural Language Toolkit.” In Proceedings of the COLING/ACL on Interactive Presentation Sessions, 69–72. Sydney, Australia. DOI logoGoogle Scholar
Bojar, Ondřej, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Varvara Logacheva, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Martin Popel, Matt Post, Raphael Rubino, Carolina Scarton, Lucia Specia, Marco Turchi, Karin Verspoor, Marcos Zampieri. 2016. “Findings of the 2016 Conference on Machine Translation (WMT16).” In Proceedings of the First Conference on Machine Translation 21: 131–198. Berlin, Germany: Association for Computational Linguistics.Google Scholar
Bowker, Lynne. 2007. “Translation Memory and ‘Text’.” In Lexicography, Terminology, and Translation. Text-Based Studies in Honour of Ingrid Meyer, edited by Lynne Bowker, 175–187. Ottawa: University of Ottawa Press.Google Scholar
Cadwell, Patrick, Sharon O’Brien, and Carlos S. C. Teixeira. 2017. “Resistance and Accommodation: Factors for the (Non-) Adoption of Machine Translation among Professional Translators.” Perspectives: Studies in Translation Theory and Practice 26 (3): 301–321. DOI logoGoogle Scholar
Cadwell, Patrick, Sheila Castilho, Sharon O’Brien, and Linda Mitchell. 2016. “Human Factors in Machine Translation and Post-Editing Among Institutional Translators.” Translation Spaces 5 (2): 222–243. DOI logoGoogle Scholar
Carl, Michael, Silke Gutermuth, and Silvia Hansen-Schirra. 2015. “Post-Editing Machine Translation: A Usability Test for Professional Translation Settings.” In Psycholinguistic and Cognitive Inquiries into Translation and Interpreting, edited by Aline Ferreira and John W. Schwieter, 145–174. Amsterdam: John Benjamins. DOI logoGoogle Scholar
Castilho, Sheila, and Sharon O’Brien. 2016. “Content Profiling and Translation Scenarios.” The Journal of Internationalization and Localization 3(1): 18–37. DOI logoGoogle Scholar
Castilho, Sheila, Joss Moorkens, Federico Gaspari, Iacer Calixto, John Tinsley, and Andy Way. 2017. “Is Neural Machine Translation the New State of the Art?” The Prague Bulletin of Mathematical Linguistics 1081: 109–120. DOI logoGoogle Scholar
Castilho, Sheila, Joss Moorkens, Federico Gaspari, Rico Sennrich, Vilelmini Sosoni, Panayota Georgakopoulou, Pintu Lohar, Andy Way, Antonio Valerio Miceli Barone, and Maria Gialama. 2017. “A Comparative Quality Evaluation of PBSMT and NMT using Professional Translators.” Conference paper presented at the MT Summit 2017. Nagoya, Japan.
Catford, John C. 1965. A Linguistic Theory of Translation: An Essay in Applied Linguistics. London: Oxford University Press.Google Scholar
Church, Kenneth W., and Eduard H. Hovy. 1993. “Good Applications for Crummy Machine Translation.” Machine Translation 8 (4): 239–258. DOI logoGoogle Scholar
Daems, Joke, Orphée De Clercq, and Lieve Macken. 2017. “Translationese and Post-Editese: How Comparable is Comparable Quality?” Linguistica Antverpiensia, New Series: Themes in Translation Studies 161: 89–103.Google Scholar
De Almeida, Giselle, and Sharon O’Brien. 2010. “Analysing Post-Editing Performance: Correlations with Years of Translation Experience.” In Proceedings of the 14th Annual Conference of the European Association for Machine Translation held in St. Raphaël, France.Google Scholar
Durrani, Nadir, Helmut Schmid, and Alexander Fraser. 2011. “A Joint Sequence Translation Model with Integrated Reordering.” In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, from June 19 to 24, in Portland, Oregon, 1045–1054.Google Scholar
Forcada, Mikel L. 2017. “Making Sense of Neural Machine Translation.” Translation Spaces 6 (2): 291–309. DOI logoGoogle Scholar
Gaspari, Federico, Antonio Toral, Sudip Kumar Naskar, Declan Groves, and Andy Way. 2014. “Perception vs Reality: Measuring Machine Translation Post-Editing Productivity.” In Proceedings of AMTA 2014 Workshop on Post-editing Technology and Practice, Vancouver, 60–72.Google Scholar
Genzel, Dmitriy, Jakob Uszkoreit, and Franz Och. 2010. “‘Poetic’ Statistical Machine Translation: Rhyme and Meter.” In the EMNLP ’10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, Cambridge, Massachusetts, 158–166. Stroudsburg: Association for Computational Linguistics.Google Scholar
Green, Spence, Jeffrey Heer, and Christopher D. Manning. 2013. “The Efficacy of Human Post-Editing for Language Translation.” In the CHI ’13 Proceedings of the SIGCHI Conference Factors in Computing Systems. New York: ACM Press. DOI logoGoogle Scholar
Greene, Erica, Tugba Bodrumlu, and Kevin Knight. 2010. “Automatic Analysis of Rhythmic Poetry with Applications to Generation and Translation.” In the EMNLP ’10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, Cambridge, Massachusetts, 524–533. Stroudsburg: Association for Computational Linguistics.Google Scholar
Guerberof, Ana. 2012. “Productivity and Quality in the Post-Editing of Outputs from Translation Memories and Machine Translation.” PhD Dissertation. Universitat Rovira i Virgili.Google Scholar
Hassan, Hany, Anthony Aue, Chang Chen, Vishal Chowdhary, Jonathan Clark, Christian Federmann, Xuedong Huang, Marcin Junczys-Dowmunt, William Lewis, Mu Li, Shujie Liu, Tie-Yan Liu, Renqian Luo, Arul Menezes, Tao Qin, Frank Seide, Xu Tan, Fei Tian, Lijun Wu, Shuangzhi Wu, Yingce Xia, Dongdong Zhang, Zhirui Zhang, and Ming Zhou. 2018. “Achieving Human Parity on Automatic Chinese to English News Translation.” Redmond: Microsoft AI & Research. arXiv:1803.05567.Google Scholar
Heyn, Matthias. 1998. “Translation Memories: Insights and Prospects.” In Unity in Diversity? Current Trends in Translation Studies, edited by Lynne Bowker, Michael Cronin, Dorothy Kenny, and Jennifer Pearson, 123–36. Manchester: St. Jerome Publishing.Google Scholar
Jones, Ruth, and Ann Irvine. 2013. “The (Un)Faithful Machine Translator.” In Proceedings of the 7th Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities, Sofia, Bulgaria, 96–101. Stroudsburg: Association for Computational Linguistics, [URL]
Kelly, Nataly. 2014. “Why So Many Translators Hate Translation Technology.” Huffington Post. The Blog. [URL]
Klubička, Filip, Antonio Toral, and Víctor M. Sánchez-Cartagena. 2017. “Fine-Grained Human Evaluation of Neural Versus Phrase-Based Machine Translation.” The Prague Bulletin of Mathematical Linguistics 1081: 121–132. DOI logoGoogle Scholar
Koehn, P., and R. Knowles. 2017. “Six Challenges for Neural Machine Translation.” In Proceedings of the First Workshop on Neural Machine Translation, Vancouver, BC, Canada, 28–39. [URL]. DOI logo
Koponen, Maarit. 2016. “Is Post-Editing Worth the Effort? A Survey of Research into Post-Editing and Effort.” JosTrans: Journal of Specialised Translation 251: 131–148.Google Scholar
Krings, Hans P. 2001. Repairing Texts: Empirical Investigations of Machine Translation Post-Editing Processes. Ohio: Kent State University Press.Google Scholar
Lacruz, Isabel, and Gregory M. Shreve. 2014. “Pauses and Cognitive Effort in Post-Editing.” In Post-Editing of Machine Translation: Processes and Applications, edited by Sharon O’Brien, Laura Winther Balling, Michael Carl, Michel Simard, and Lucia Specia, 287–314. Newcastle-Upon-Tyne: Cambridge Scholars.Google Scholar
LeBlanc, Matthieu. 2013. “Translators on Translation Memory (TM). Results of an Ethnographic Study in Three Translation Services and Agencies.” The International Journal for Translation and Interpreting Research 5 (2): 1–13. DOI logoGoogle Scholar
Lommel, Arle, and Donald A. DePalma. 2016. “Europe’s Leading Role in Machine Translation: How Europe Is Driving the Shift to MT.” Technical Report. Boston: Common Sense Advisory.Google Scholar
Martín, Juan Alberto Alonso, and Anna Civil Serra. 2014. “Integration of a Machine Translation System into the Editorial Process Flow of a Daily Newspaper.” Procesamiento del Lenguaje Natural Revista 531: 193–196.Google Scholar
Moorkens, Joss. 2017. “Under Pressure: Translation in Times of Austerity.” Perspectives 25 (3): 464–477. DOI logoGoogle Scholar
Moorkens, Joss, and Sharon O’Brien. 2015. “Post-Editing Evaluations: Trade-offs between Novice and Professional Participants.” In Proceedings of the 18th Annual Conference of the European Association for Machine Translation (EAMT 2015), edited by İIknur Durgar El-Kahlout, Mehmed Özkan, Felipe Sánchez-Martínez, Gema Ramírez-Sánchez, Fred Hollowood, and Andy Way, 75–81.Google Scholar
. 2017. “Assessing User Interface Needs of Post-Editors of Machine Translation.” In Human Issues in Translation Technology: The IATIS Yearbook, edited by Dorothy Kenny, 109–130, Oxford, United Kingdom: Routledge.Google Scholar
Moorkens, Joss, Sharon O’Brien, Igor A. L. Silva, Norma Fonseca, and Fabio Alves. 2015. “Correlations of perceived post-editing effort with measurements of actual effort.” Machine Translation 29 (3–4): 267–284. DOI logoGoogle Scholar
Nida, Eugene. 1964. Towards a Science of Translating. Leiden: Brill.Google Scholar
Nitzke, Jean. 2016. “Monolingual Post-Editing: An Exploratory Study on Research Behaviour and Target Text Quality.” In Eye-tracking and Applied Linguistics, edited by Silvia Hansen-Schirra, and Sambor Grucza, 83–109. Berlin: Language Science Press. DOI logoGoogle Scholar
PACTE group. 2005. “Investigating Translation Competence: Conceptual and Methodological Issues.” Meta 50 (2): 609–619. DOI logoGoogle Scholar
Plitt, Mirko, and François Masselot. 2010. “A Productivity Test of Statistical Machine Translation Post-Editing in a Typical Localisation Context.” The Prague Bulletin of Mathematical Linguistics 931: 7–16. DOI logoGoogle Scholar
Pym, Anthony. 2008. “Professional Corpora: Teaching Strategies for Work with Online Documentation, Translation Memories and Content Management.” Chinese Translator’s Journal 29 (2): 41–45.Google Scholar
Reiss, Katharina. 1981. “Type, Kind and Individuality of Text: Decision Making in Translation.” Poetics Today 2 (4): 121–131. DOI logoGoogle Scholar
Sennrich, Rico, Barry Haddow, and Alexandra Birch. 2016a. “Improving Neural Machine Translation Models with Monolingual Data.” In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics from August 7 to August 12, 2016, 86–96. Berlin, Germany.Google Scholar
. 2016b. “Neural Machine Translation of Rare Words with Subword Units.” In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics from August 7 to August 12, 2016, 1715–1725. Berlin, Germany.Google Scholar
. 2016c. “Controlling Politeness in Neural Machine Translation via Side Constraints.” In Proceedings of NAACL-HLT 2016, 351–40.Google Scholar
Sennrich, Rico, Orhan Firat, Kyunghyun Cho, Alexandra Birch, Barry Haddow, Julian Hitschler, Marcin Junczys-Dowmunt, Samuel Läubli, Antonio Valerio Miceli Barone, Jozef Mokry, and Maria Nadejde. 2017. “Nematus: A Toolkit for Neural Machine Translation.” In Proceedings of the Software Demonstrations from the 15th Conference of the European Chapter of the Association for Computational Linguistics, 65–68. [URL]. DOI logo
Somers, Harold. 2001. Computers and Translation: A Translator’s Guide. Amsterdam: John Benjamins. DOI logoGoogle Scholar
Snover, Matthew, Bonnie Dorr, Richard Schwartz, Linnea Micciulla, and John Makhoul. 2006. “A Study of Translation Edit Rate with Targeted Human Annotation.” Proceedings of Association for Machine Translation in the Americas.Google Scholar
Specia, Lucia. 2011. “Exploiting Objective Annotations for Measuring Translation Post-Editing Effort.” In Proceedings of the 15th Conference of the European Association for Machine Translation, 73–80. Leuven, Belgium.Google Scholar
Specia, Lucia, and Kashif Shah. 2018. “Machine Translation Quality Estimation: Applications and Future Perspectives.” In Translation Quality Assessment: From Principles to Practice, edited by Joss Moorkens, Sheila Castilho, Federico Gaspari, and Stephen Doherty, 201–236. Heidelberg: Springer International Publishing. DOI logoGoogle Scholar
Taivalkoski-Shilov, Kristiina. 2018. “Ethical Issues Regarding Machine(-assisted) Translation of Literary Texts.” Perspectives: Studies in Translation Theory and Practice (online first). Special Issue: Voice, Translation, and Ethics, ed. by Cecilia Alvstad, Annjo K. Greenall, Hanne Jansen, and Kristiina Taivalkoski-Shilov. DOI logoGoogle Scholar
Teixeira, Carlos S. C. 2014. “Perceived vs. Measured Performance in the Post-Editing of Suggestions from Machine Translation and Translation Memories.” In Proceedings of the Third Workshop on Post-Editing Technology and Practice (WPTP-3), edited by Sharon O’Brien, Michel Simard, and Lucia Specia, 45–59.Google Scholar
Thouin, Benoît. 1982. “The METEO System.” In Practical Experience of Machine Translation: Proceedings of Translating and the Computer 1981, edited by Veronica Lawson, 39–44, Amsterdam: North-Holland Publishing.Google Scholar
Toral, Antonio, and Andy Way. 2015. “Translating Literary Text between Related Languages using SMT.” In Proceedings of NAACL-HLT Fourth Workshop on Computational Linguistics for Literature, 123–132. Denver, Colorado. DOI logoGoogle Scholar
. 2018. “What level of quality can Neural Machine Translation attain on literary text?” In Translation Quality Assessment: From Principles to Practice, edited by Joss Moorkens, Sheila Castilho, Federico Gaspari, and Stephen Doherty, 263–287. Heidelberg: Springer International Publishing. DOI logoGoogle Scholar
Toral, Antonio, and Victor M. Sánchez-Cartagena. 2017. “A Multifaceted Evaluation of Neural versus Phrase-Based Machine Translation for 9 Language Directions.” In Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017. Valencia, Spain. DOI logoGoogle Scholar
Toral, Antonio, Martijn Wieling, and Andy Way. 2018. “Post-editing Effort of a Novel with Statistical and Neural Machine Translation.” Frontiers in Digital Humanities 51:9. DOI logoGoogle Scholar
Vasconcellos, Muriel. 1985. “Machine Aids to Translation: A Holistic Scenario for Maximizing the Technology.” In Overcoming Language Barriers: The Human/Machine Relationship, Proceedings of the IV Annual Conference on Language and Communication held from December 13 to December 14, 1985 in New York, edited by Humphrey Tonkin, and Karen Johnson-Weiner, 27–34. New York: Center for Research and Documentation on World Problems.Google Scholar
Vaswani, Ashish, Yinggong Zhao, Victoria Fossum and David Chiang. 2013. “Decoding with Large-Scale Neural Language Models Improves Translation.” In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, Seattle, Washington, 1387–1392. Stroudsburg: Association for Computational Linguistics.Google Scholar
Viera, Lucas Nunes. 2014. “Indices of cognitive effort in machine translation post-editing.” Machine Translation 28 (3–4):187–216. DOI logoGoogle Scholar
Wagner, Elizabeth. 1985. “Post-Editing Systran-A Challenge for Commission Translators.” Terminologie et Traduction 31: 1–7.Google Scholar
Way, Andy. 2013. “Traditional and Emerging Use-Cases for Machine Translation.” In Proceedings of Translating and the Computer 351. London, United Kingdom.Google Scholar
. 2018a. “Quality Expectations of Machine Translation.” In Translation Quality Assessment: From Principles to Practice, edited by Joss Moorkens, Sheila Castilho, Federico Gaspari, and Stephen Doherty, 159–178. Heidelberg: Springer International Publishing. DOI logoGoogle Scholar
. 2018b. “Machine Translation: Where We Are at Today.” In The Bloomsbury Companion to Language Industry Studies, edited by Erik Angelone, Gary Massey, and Maureen Ehrensberger-Dow. London: Bloomsbury.Google Scholar
Wu, Yonghui, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2016. “Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation”. arXiv preprint 1609.08144, [URL]
Cited by (40)

Cited by 40 other publications

Corpas Pastor, Gloria & Laura Noriega-Santiáñez
2024. Human versus Neural Machine Translation Creativity: A Study on Manipulated MWEs in Literature. Information 15:9 ► pp. 530 ff. DOI logo
Cui, Ying, Xiao Liu & Wei Wang
2024. Reliability of Effort Indicators for Human Translation and Post-editing of Machine Translation: An Eye Tracking and Keylogging Study Involving Student Translators. In Emerging Technologies for Education [Lecture Notes in Computer Science, 14606], ► pp. 207 ff. DOI logo
Gürses, Sabri, Mehmet Şahin, Ena Hodzik, Tunga Güngör, Harun Dallı & Olgun Dursun
2024. Çeviribilim Çalışmalarında Çevirmenin Üslubu ve Makinenin Üslubu. Çeviribilim ve Uygulamaları Dergisi :36 ► pp. 100 ff. DOI logo
Hao, Yu, Ke Hu & Anthony Pym
2024. Who’s afraid of literary post-editing? Performances and Reflections of Student Translators. In New Advances in Translation Technology [New Frontiers in Translation Studies, ], ► pp. 263 ff. DOI logo
Li, Xue
2024. A Model for Assessing the Impact of Intelligent Algorithms on the Translation Quality of Literary Works in Cross-Cultural Communication. Applied Mathematics and Nonlinear Sciences 9:1 DOI logo
Ruffo, Paola
2024. Literary translators in-between. Translation in Society 3:1 ► pp. 87 ff. DOI logo
Ruffo, Paola
2024. Literary translators and technology: SCOT as a proactive and flexible approach. Perspectives 32:3 ► pp. 407 ff. DOI logo
Tan, Fangmin & Huaju Wang
2024. A Semantic Context-Aware Automatic Quality Scoring Method for Machine Translation Based on Pretraining Language Model. IEEE Access 12 ► pp. 72023 ff. DOI logo
Ye, Chunbo
2024. Semantic Accuracy and Cultural Adaptability in the English-Chinese Translation of Jane Eyre Based on Computational Linguistics and Natural Language Processing Techniques. Applied Mathematics and Nonlinear Sciences 9:1 DOI logo
Zavala-Rojas, Diana, Dorothée Behr, Brita Dorer, Danielly Sorato & Veronika Keck
2024. Using Machine Translation and Post-Editing in the TRAPD Approach: Effects on the Quality of Translated Survey Texts. Public Opinion Quarterly 88:1 ► pp. 123 ff. DOI logo
Arnáiz-Uzquiza, Verónica & Paula Igareda González
2023. Traducción Automática y Posedición en TAV: estudio piloto de una práctica en alza. TRANS: Revista de Traductología :27 ► pp. 197 ff. DOI logo
Bo, Li
2023. Ethical issues for literary translation in the Era of artificial intelligence. Babel. Revue internationale de la traduction / International Journal of Translation DOI logo
Cui, Ying, Xiao Liu & Yuqin Cheng
2023. A Comparative Study on the Effort of Human Translation and Post-Editing in Relation to Text Types: An Eye-Tracking and Key-Logging Experiment. Sage Open 13:1 DOI logo
Hongtao, Wang
2023. Defending the last bastion. Babel. Revue internationale de la traduction / International Journal of Translation DOI logo
Lİ, Qing & Tai-yi HUANG
2023. Scaling Students’ Self-Efficacy on Machine Translation Post-Editing: Psychometric Properties of the Scale and Their Associations. Participatory Educational Research 10:6 ► pp. 229 ff. DOI logo
Sarkar, Sagnik, Pardeep Singh, Namrata Kumari & Poonam Kashtriya
2023. The Task of Question Answering in NLP: A Comprehensive Review. In Proceedings of International Conference on Recent Innovations in Computing [Lecture Notes in Electrical Engineering, 1011], ► pp. 603 ff. DOI logo
Wang, Bo
2023. 2023 International Conference on Data Science and Network Security (ICDSNS), ► pp. 1 ff. DOI logo
Wang, Ruomu
2023. 2023 International Conference on Data Science and Network Security (ICDSNS), ► pp. 1 ff. DOI logo
Yang, Yanxia, Runze Liu, Xingmin Qian & Jiayue Ni
2023. Performance and perception: machine translation post-editing in Chinese-English news translation by novice translators. Humanities and Social Sciences Communications 10:1 DOI logo
Castilho, Sheila & Natália Resende
2022. Post-Editese in Literary Translations. Information 13:2 ► pp. 66 ff. DOI logo
Dorst, Aletta G., Susana Valdez & Heather Bouman
2022. Machine translation in the multilingual classroom. Translation and Translanguaging in Multilingual Contexts 8:1 ► pp. 49 ff. DOI logo
Ragni, Valentina & Lucas Nunes Vieira
2022. What has changed with neural machine translation? A critical review of human factors. Perspectives 30:1 ► pp. 137 ff. DOI logo
Rico, Celia
2022. Mind the gap. Babel. Revue internationale de la traduction / International Journal of Translation 68:5 ► pp. 697 ff. DOI logo
Sakamoto, Akiko
2022. Translation and Technology. In The Cambridge Handbook of Translation, ► pp. 55 ff. DOI logo
Sánchez-Gijón, Pilar
2022. Neural machine translation and the indivisibility of culture and language. FORUM. Revue internationale d’interprétation et de traduction / International Journal of Interpretation and Translation 20:2 ► pp. 357 ff. DOI logo
Zheng, Jianwei & Wenjun Fan
2022. Multivaried acceptance of post-editing in China. Pragmatics and Society 13:4 ► pp. 644 ff. DOI logo
Tardel, Anke
2021. Measuring Effort in Subprocesses of Subtitling. In Explorations in Empirical Translation Process Research [Machine Translation: Technologies and Applications, 3], ► pp. 81 ff. DOI logo
Yang, Yanxia, Xiangling Wang & Qingqing Yuan
2021. Measuring the usability of machine translation in the classroom context. Translation and Interpreting Studies 16:1 ► pp. 101 ff. DOI logo
Zhao, Shengfang
2021. Post-editing Neural Machine Translation Versus Human Translation for Chinese Essays: A Pilot Study. In Diverse Voices in Chinese Translation and Interpreting [New Frontiers in Translation Studies, ], ► pp. 399 ff. DOI logo
Abdel Latif, Muhammad M. M.
2020. Researching Professional Translator/Interpreter Experiences and Roles. In Translator and Interpreter Education Research [New Frontiers in Translation Studies, ], ► pp. 125 ff. DOI logo
Guerberof-Arenas, Ana & Antonio Toral
2020. The impact of post-editing and machine translation on creativity and reading experience. Translation Spaces 9:2 ► pp. 255 ff. DOI logo
Guerberof-Arenas, Ana & Antonio Toral
2022. Creativity in translation. Translation Spaces 11:2 ► pp. 184 ff. DOI logo
Jiménez-Crespo, Miguel A.
2020. The “technological turn” in translation studies. Translation Spaces 9:2 ► pp. 314 ff. DOI logo
Kenny, Dorothy & Marion Winters
2020. Machine translation, ethics and the literary translator’s voice. Translation Spaces 9:1 ► pp. 123 ff. DOI logo
Nunes Vieira, Lucas & Elisa Alonso
2020. Translating perceptions and managing expectations: an analysis of management and production perspectives on machine translation. Perspectives 28:2 ► pp. 163 ff. DOI logo
O’Brien, Sharon & Maureen Ehrensberger-Dow
2020. MT Literacy—A cognitive view. Translation, Cognition & Behavior 3:2 ► pp. 145 ff. DOI logo
Sakamoto, Akiko & Masaru Yamada
2020. Social groups in machine translationpost-editing. Translation Spaces 9:1 ► pp. 78 ff. DOI logo
Slessor, Stephen
2020. Tenacious technophobes or nascent technophiles? A survey of the technological practices and needs of literary translators. Perspectives 28:2 ► pp. 238 ff. DOI logo
Chang-Soo Lee
2019. 문학번역에서의 기계번역과 인간번역 문체에 대한 전산문체학적 비교 연구. The Journal of Translation Studies 20:2 ► pp. 111 ff. DOI logo
Lee, Changsoo
2022. How do machine translators measure up to human literary translators in stylometric tests?. Digital Scholarship in the Humanities 37:3 ► pp. 813 ff. DOI logo

This list is based on CrossRef data as of 8 november 2024. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.