| dc.identifier.citation | Alotaibi, H. (2014). Teaching CAT Tools to Translation Students: an Examination of Their Expectations and Attitudes. AWEJ, 3, 65–74.
Anastasiou, D. (2008). Idioms in example-based machine translation. https://www.academia.edu/30082514/Idioms_in_example_based_machine_translation
Aranberri, N. (2017). What Do Professional Translators Do when Post-Editing for the First Time? First Insight into the Spanish-Basque Language Pair. HERMES - Journal of Language and Communication in Business, 56, 89–110. https://doi.org/10.7146/hjlcb.v0i56.97235
Aziz, W., Castilho, S., & Specia, L. (2012). PET: a Tool for Post-editing and Assessing Machine Translation. Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC’12), 3982–3987. http://www.lrec-conf.org/proceedings/lrec2012/pdf/985_Paper.pdf
Bundgaard, K., & Christensen, T. P. (2019). Is the concordance feature the new black? A workplace study of translators’ interaction with translation resources while post-editing TM and MT matches. The Journal of Specialised Translation, 31, 14–37.
Cadwell, P., O’Brien, S., & Teixeira, C. S. C. (2018). Resistance and accommodation: factors for the (non-) adoption of machine translation among professional translators. Perspectives: Studies in Translatology, 26(3), 301–321. https://doi.org/10.1080/0907676X.2017.1337210
Carl, M., Dragsted, B., Elming, J., Hardt, D., & Jakobsen, A. L. (2011). The Process of Post-Editing: a Pilot Study. 8th International NLPCS Workshop, August, 131–142.
CAT Tools | Software Comparison Tool. (n.d.). Retrieved May 18, 2022, from https://www.proz.com/software-comparison-tool/cat/cat_tools/2
Çetiner, C. (2019). Makine çevirisi sonrası düzeltme işleminin çeviri öğrencilerinin tutum ve çeviri performanslarına etkisi [Unpublished PhD Thesis, Gazi University]. https://doi.org/10.29000/rumelide.649333
Chemvura, T. (2017). LARMAS - Language Resource Management System. https://doi.org/10.13140/RG.2.2.34784.38405
Daems, J., Vandepitte, S., Hartsuiker, R. J., & Macken, L. (2017). Translation Methods and Experience: A Comparative Analysis of Human Translation and Post-editing with Students and Professional Translators. Meta: Journal Des Traducteurs, 62(2), 245. https://doi.org/10.7202/1041023ar
Denkowski, M., & Lavie, A. (2011). Meteor 1.3: Automatic metric for reliable optimization and evaluation of machine translation systems. Proceedings of the Sixth Workshop on Statistical Machine Translation, 85–91. https://doi.org/10.1080/00288306.2004.9515087
Depraetere, I., Sutter, N. de, & Tezcan, A. (2014). Post-edited quality, post-editing behaviour and human evaluation: a case study. 78-108. https://hal.archives-ouvertes.fr/halshs-01060447
Diño, G. (2018). Google, Facebook, Amazon: Neural Machine Translation Just Had Its Busiest Month Ever | Slator. Slator. https://slator.com/technology/google-facebook-amazon-neural-machine-translation-just-had-its-busiest-month-ever/
DuPont, Q. (2018). The Cryptological Origins of Machine Translation. AModern. https://amodern.net/article/cryptological-origins-machine-translation/
Egdom, G. van, & Pluymaekers, M. (2019). Why go the extra mile? How different degrees of post-editing affect perceptions of texts, senders and products among end users. The Journal of Specialised Translation, 31, 158–176.
Farooq, U. (2018, March 21). Neural Machine Translation with Code. Medium. https://medium.com/@umerfarooq_26378/neural-machine-translation-with-code-68c425044bbd
Fiederer, R., & O’Brien, S. (2009). Quality and Machine Translation: A realistic objective? The Journal of Specialised Translation, 11, 52–74.
Flanagan, M., & Christensen, T. P. (2014). Testing post-editing guidelines: how translation trainees interpret them and how to tailor them for translator training purposes. The Interpreter and Translator Trainer, 8(2), 257–275. https://doi.org/10.1080/1750399X.2014.936111
Folaron, D. A. (2010). Networking and volunteer translators. In Handbook of Translation Studies (pp. 231–234). John Benjamins Publishing Company. https://doi.org/10.1075/hts.1.net1
Garcia, I. (2010). Is machine translation ready yet? Target, 22(1), 7–21. https://doi.org/10.1075/target.22.1.02gar
Gaspari, F., Toral, A., Kumar Naskar, S., Groves, D., & Way, A. (2014). Perception vs Reality: Measuring Machine Translation Post-Editing Productivity. AMTA 2014.
Gerlach, J., Porro Rodriguez, V., Bouillon, P., & Lehmann, S. (2013). Combining pre-editing and post-editing to improve SMT of user-generated content. Proceedings of MT Summit XIV Workshop on Post-Editing Technology and Practice, 2(1), 45–53.
Gross, A. (1992). Limitations of computers as translation tools. In J. Newton (Ed.), Computers in Translation: A Practical Appraisal. Routledge.
Guerberof Arenas, A. (2008). Productivity and quality in the post-editing of outputs from translation memories and machine translation. Localisation Focus The International Journal of Localisation, 7(1), 11–21.
Hutchins, J. (1999). Milestones in machine translation — No.6: Bar-Hillel and the nonfeasibility of FAHQT. http://www.mt-archive.info/Weaver-1949.pdf
Hutchins, W. J. (1986). Machine translation: past, present, future. Ellis Horwood ; Halsted Press.
Jia, Y., Carl, M., & Wang, X. (2019). How does the post-editing of neural machine translation compare with from-scratch translation? A product and process study. Journal of Specialised Translation, 31, 60–86.
Koehn, P. (2016). CASMACAT: Advanced Computer Aided Translation Beyond Postediting. https://ec.europa.eu/info/sites/info/files/tef2016_koehn_en.pdf
Koponen, M. (2012). Comparing human perceptions of post-editing effort with post-editing operations. 181–190. http://www.statmt.org/wmt12/
Koponen, M. (2015). How to teach machine translation post-editing ? Experiences from a post-editing course. Proceedings of 4th Workshop on Post-Editing Technology and Practice (WPTP4).
Koponen, M. (2018). Learning to post-edit: An analysis of post-editing quality and processes of translation students. International Association for Translation and Intercultural Studies (IATIS) 6th International Conference, July. https://doi.org/10.13140/RG.2.2.24675.04648
Koponen, M., Aziz, W., Ramos, L., & Specia, L. (2012). Post-editing time as a measure of cognitive effort. AMTA Workshop on Postediting Technology and Practice, 47(3), 11–20. https://doi.org/10.1111/j.1469-8986.2009.00947.x.Pupillometry
Koponen, M., & Salmi, L. (2017). Post-editing quality: Analysing the correctness and necessity of post-editor corrections. Linguistica Antverpiensia, New Series: Themes in Translation Studies, 16, 137–148.
Krings, H. P. (2001). Repairing Texts: Empirical Investigations of Machine Translation Post-editing Processes (G. S. Koby, Ed.). The Kent State University Press. https://books.google.com.tr/books?id=vsdPsIXCiWAC
Lacruz, I., & Jääskeläinen, R. (2018). Innovation and Expansion in Translation Process Research. John Benjamins Publishing Company.
Lacruz, I., Shreve, G. M., & Angelone, E. (2012). Average Pause Ratio as an Indicator of Cognitive Effort in Post-Editing: A Case Study. AMTA 2012 Workshop on Post-Editing Technology and Practice (WPTP 2012), 21–30.
Lardilleux, A., & Lepage, Y. (2017). CharCut: Human-Targeted Character-Based MT Evaluation with Loose Differences. Proceedings of IWSLT 2017.
Läubli, S., Fishel, M., Massey, G., Ehrensberger-Dow, M., & Volk, M. (2013, September). Assessing post-editing efficiency in a realistic translation environment. Proceedings of the 2nd Workshop on Post-Editing Technology and Practice. https://aclanthology.org/2013.mtsummit-wptp.10
Lauscher, S. (2000). Translation Quality Assessment. The Translator, 6, 149–168. https://doi.org/10.1080/13556509.2000.10799063
Le, Q. v., & Schuster, M. (2016, September 27). A Neural Network for Machine Translation, at Production Scale. Google AI Blog. https://ai.googleblog.com/2016/09/a-neural-network-for-machine.html
Lilly, P. (2016, September 28). Google Translate Has Reached Human-Like Accuracy Thanks To Neural Machine Translation Engine. HotHardware. https://hothardware.com/news/google-translate-human-like-accuracy-neural-machine-translation-engine
Lommel, A. (2016, June 22). MT is Changing the Industry, Just Not in the Way Mainstream Media Thinks It Will. https://csa-research.com/Blogs-Events/Blog/MT-is-Changing-the-Industry-Just-Not-in-the-Way-Mainstream-Media-Thinks-It-Will
Massardo, I., van der Meer, J., O’Brien, S., Hollowood, F., Aranberri, N., & Drescher, K. (2016). MT POST-EDITING GUIDELINES. TAUS Signature Editions.
Milengo GmbH. (2019, January 30). How NMT-Based Translation Services Can Reduce Enterprise Translation Costs by up to 80%. Slator. https://slator.com/how-nmt-based-translation-services-can-reduce-enterprise-translation-costs-by-up-to-80/
Moorkens, J., O’Brien, S., da Silva, I. A. L., de Lima Fonseca, N. B., & Alves, F. (2015). Correlations of perceived post-editing effort with measurements of actual effort. Machine Translation, 29(3–4), 267–284. https://doi.org/10.1007/s10590-015-9175-2
Nitzke, J., & Oster, K. (2016). Comparing Translation and Post-editing: An Annotation Schema for Activity Units. In M. Carl, S. Bangalore, & M. Schaeffer (Eds.), New Directions in Empirical Translation Process Research. New Frontiers in Translation Studies. Springer, Cham. https://doi.org/https://doi.org/10.1007/978-3-319-20358-4_14
O’Brien, S. (2002). Teaching Post-Editing: A Proposal for Course Content. Proceedings of the 6th EAMT Workshop: Teaching Machine Translation. https://aclanthology.org/2002.eamt-1.11
O’Brien, S. (2006). Pauses as Indicators of Cognitive Effort in Post-editing Machine Translation Output. Across Languages and Cultures, 7(1), 1–21. https://doi.org/10.1556/acr.7.2006.1.1
O’Brien, S. (2012). Translation as human–computer interaction. Translation Spaces, 1, 101–122. https://doi.org/10.1075/ts.1.05obr
Paas, F., Tuovinen, J. E., Tabbers, H., & van Gerven, P. W. M. (2003). Cognitive Load Measurement as a Means to Advance Cognitive Load Theory. Educational Psychologist, 38(1), 63–71. https://doi.org/10.1207/S15326985EP3801_8
Plitt, M., & Masselot, F. (2010). A Productivity Test of Statistical Machine Translation Post-Editing in a Typical Localisation Context. The Prague Bulletin of Mathematical Linguistics, 93(1), 7–16. https://doi.org/10.2478/v10108-010-0010-x
Poibeau, T. (2017). Machine Translation. Cambridge, MA: The MIT Press.
Popović, M., Lommel, A., Burchardt, A., Avramidis, E., & Uszkoreit, H. (2014). Relations between different types of post-editing operations, cognitive effort and temporal effort. Proceedings of the 17th Annual Conference of the European Association for Machine Translation, 191–198. https://www.researchgate.net/publication/268446855%0Ahttp://www.dfki.de/web/forschung/publikationen/renameFileForDownload?filename=finalVersion48.pdf&file_id=uploads_2255
Pym, A. (2013). Translation Skill-Sets in a Machine-Translation Age. Meta: Journal Des Traducteurs, 58, 487. https://doi.org/10.7202/1025047ar
R Core Team. (2018). R: A Language and Environment for Statistical Computing. https://www.r-project.org/
Rico, C., & Torrejón, E. (2012). Skills and Profile of the New Role of the Translator as MT Post-editor. Revista Tradumàtica: Tecnologies de La Traducció, 2012(10), 166–178. http://revistes.uab.cat/http://revistes.uab.cat/tradumatica
Sánchez-Gijón, P., Moorkens, J., & Way, A. (2019). Post-editing neural machine translation versus translation memory segments. Machine Translation, 33(1–2), 31–59. https://doi.org/10.1007/s10590-019-09232-x
Sankaravelayuthan, R., & Vasuki, G. (2013). English to Tamil machine translation system using parallel corpus. LAP LAMBERT Academic Publishing. https://www.perlego.com/book/3413858/english-to-tamil-machine-translation-system-using-parallel-corpus-pdf
Schwartz, L. (2018). The history and promise of machine translation. American Translators Association Scholarly Monograph Series, 18, 161–190. https://doi.org/10.1075/ata.18.08sch
Screen, B. (2019). What effect does post-editing have on the translation product from an end- user’s perspective? The Journal of Specialised Translation, 500(31), 133–157.
Sin-wai, C. (2017). The Future of Translation Technology: Towards a World without Babel. Routledge.
Stefaniak, K. (n.d.). Post-editing in DGT. European Commission, Directorate-General for Translation.
Sukkhwan, A., & Sripetpun, W. (2014). Students’ attitudes and behaviors towards the use of google translate [Unpublished master’s thesis]. Prince of Songkla University.
Teixeira, C. S. C. (2014). Perceived vs. measured performance in the post-editing of suggestions from machine translation and translation memories. Proceedings of the 11th Conference of the Association for Machine Translation in the Americas, 45–59. https://aclanthology.org/2014.amta-wptp.4
Temizöz, Ö. (2012). Machine translation and postediting. Est-Translationstudies.Org, 19. http://www.est-translationstudies.org/intranet/research/MT.pdf
Temizöz, Ö. (2016). Postediting machine translation output: subject-matter experts versus professional translators. Perspectives: Studies in Translatology, 24(4), 646–665. https://doi.org/10.1080/0907676X.2015.1119862
Thames, J. (2019, June 24). Machine Translation. LanguageSolutions. https://langsolinc.com/machine-translation/
Toral, A., Wieling, M., & Way, A. (2018). Post-editing Effort of a Novel With Statistical and Neural Machine Translation. Frontiers in Digital Humanities, 5. https://doi.org/10.3389/fdigh.2018.00009
Trados Studio - Translation Software. (n.d.). Retrieved May 18, 2022, from https://www.trados.com/products/trados-studio/
Turovsky, B. (2016, November 15). Found in translation: More accurate, fluent sentences in Google Translate. The Keyword. https://blog.google/products/translate/found-translation-more-accurate-fluent-sentences-google-translate/
Vanroy, B., de Clercq, O., & Macken, L. (2019). Correlating process and product data to get an insight into translation difficulty. Perspectives: Studies in Translation Theory and Practice, 27(6), 924–941. https://doi.org/10.1080/0907676X.2019.1594319
Vieira, L. N. (2016). How do measures of cognitive effort relate to each other? A multivariate analysis of post-editing process data. Machine Translation, 30(1–2), 41–62. https://doi.org/10.1007/s10590-016-9188-5
Vieira, L. N. (2018). Automation anxiety and translators. Translation Studies, 0(0), 1–21. https://doi.org/10.1080/14781700.2018.1543613
Yamada, M. (2019). The impact of Google Neural Machine Translation on Post-editing by student translators. The Journal of Specialised Translation, 31, 87–106. | tr_TR |
