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

References

1. Aziz, Wilker, Sheila Castilho M. de Sousa, and Lucia Specia
   2012 “PET: a tool for post-editing and assessing machine translation.” InThe Eighth International Conference on Language Resources and Evaluation, LREC ‘12, Istanbul, Turkey. May 2012. www.lrec-conf.org/proceedings/lrec2012/pdf/985_Paper.pdf
   [Google Scholar]
2. Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio
   2014 Neural Machine Translation by Jointly Learning to Align and Translate. Conference paper presented atthe ICLR 2015. arXiv preprint, arXiv:1409.0473.
   [Google Scholar]
3. Bar-Hillel, Yehoshua
   1960 “The Present Status of Automatic Translation of Languages.” Advances in Computers1: 91–163. 10.1016/S0065‑2458(08)60607‑5
   https://doi.org/10.1016/S0065-2458(08)60607-5 [Google Scholar]
4. 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 & Language49: 52–70. 10.1016/j.csl.2017.11.004
   https://doi.org/10.1016/j.csl.2017.11.004 [Google Scholar]
5. Besacier, Laurent
   2014 “Traduction automatisée d’une œuvre littéraire: une étude pilote.” InTraitement Automatique du Langage Naturel (TALN). Marseille, France.
   [Google Scholar]
6. Bird, Steven
   2006 “NLTK: The Natural Language Toolkit.” InProceedings of the COLING/ACL on Interactive Presentation Sessions, 69–72. Sydney, Australia. 10.3115/1225403.1225421
   https://doi.org/10.3115/1225403.1225421 [Google Scholar]
7. 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).” InProceedings of the First Conference on Machine Translation2: 131–198. Berlin, Germany: Association for Computational Linguistics.
   [Google Scholar]
8. Bowker, Lynne
   2007 “Translation Memory and ‘Text’.” InLexicography, Terminology, and Translation. Text-Based Studies in Honour of Ingrid Meyer, edited byLynne Bowker, 175–187. Ottawa: University of Ottawa Press.
   [Google Scholar]
9. 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 Practice26 (3): 301–321. 10.1080/0907676X.2017.1337210
   https://doi.org/10.1080/0907676X.2017.1337210 [Google Scholar]
10. Cadwell, Patrick, Sheila Castilho, Sharon O’Brien, and Linda Mitchell
    2016 “Human Factors in Machine Translation and Post-Editing Among Institutional Translators.” Translation Spaces5 (2): 222–243. 10.1075/ts.5.2.04cad
    https://doi.org/10.1075/ts.5.2.04cad [Google Scholar]
11. Carl, Michael, Silke Gutermuth, and Silvia Hansen-Schirra
    2015 “Post-Editing Machine Translation: A Usability Test for Professional Translation Settings.” InPsycholinguistic and Cognitive Inquiries into Translation and Interpreting, edited byAline Ferreira and John W. Schwieter, 145–174. Amsterdam: John Benjamins. 10.1075/btl.115.07car
    https://doi.org/10.1075/btl.115.07car [Google Scholar]
12. Castilho, Sheila, and Sharon O’Brien
    2016 “Content Profiling and Translation Scenarios.” The Journal of Internationalization and Localization3(1): 18–37. 10.1075/jial.3.1.02cas
    https://doi.org/10.1075/jial.3.1.02cas [Google Scholar]
13. 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 Linguistics108: 109–120. 10.1515/pralin‑2017‑0013
    https://doi.org/10.1515/pralin-2017-0013 [Google Scholar]
14. 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 theMT Summit 2017. Nagoya, Japan.
    [Google Scholar]
15. Catford, John C.
    1965 A Linguistic Theory of Translation: An Essay in Applied Linguistics. London: Oxford University Press.
    [Google Scholar]
16. Church, Kenneth W., and Eduard H. Hovy
    1993 “Good Applications for Crummy Machine Translation.” Machine Translation8 (4): 239–258. 10.1007/BF00981759
    https://doi.org/10.1007/BF00981759 [Google Scholar]
17. 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 Studies16: 89–103.
    [Google Scholar]
18. De Almeida, Giselle, and Sharon O’Brien
    2010 “Analysing Post-Editing Performance: Correlations with Years of Translation Experience.” InProceedings of the 14th Annual Conference of the European Association for Machine Translation held in St. Raphaël, France.
    [Google Scholar]
19. Durrani, Nadir, Helmut Schmid, and Alexander Fraser
    2011 “A Joint Sequence Translation Model with Integrated Reordering.” InProceedings 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]
20. Forcada, Mikel L.
    2017 “Making Sense of Neural Machine Translation.” Translation Spaces6 (2): 291–309. 10.1075/ts.6.2.06for
    https://doi.org/10.1075/ts.6.2.06for [Google Scholar]
21. Gaspari, Federico, Antonio Toral, Sudip Kumar Naskar, Declan Groves, and Andy Way
    2014 “Perception vs Reality: Measuring Machine Translation Post-Editing Productivity.” InProceedings of AMTA 2014 Workshop on Post-editing Technology and Practice, Vancouver, 60–72.
    [Google Scholar]
22. Genzel, Dmitriy, Jakob Uszkoreit, and Franz Och
    2010 “‘Poetic’ Statistical Machine Translation: Rhyme and Meter.” In theEMNLP ’10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, Cambridge, Massachusetts, 158–166. Stroudsburg: Association for Computational Linguistics.
    [Google Scholar]
23. Green, Spence, Jeffrey Heer, and Christopher D. Manning
    2013 “The Efficacy of Human Post-Editing for Language Translation.” In theCHI ’13 Proceedings of the SIGCHI Conference Factors in Computing Systems. New York: ACM Press. 10.1145/2470654.2470718
    https://doi.org/10.1145/2470654.2470718 [Google Scholar]
24. Greene, Erica, Tugba Bodrumlu, and Kevin Knight
    2010 “Automatic Analysis of Rhythmic Poetry with Applications to Generation and Translation.” In theEMNLP ’10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, Cambridge, Massachusetts, 524–533. Stroudsburg: Association for Computational Linguistics.
    [Google Scholar]
25. Guerberof, Ana
    2012 “Productivity and Quality in the Post-Editing of Outputs from Translation Memories and Machine Translation.” PhD Dissertation. Universitat Rovira i Virgili.
26. 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.
27. Heyn, Matthias
    1998 “Translation Memories: Insights and Prospects.” InUnity in Diversity? Current Trends in Translation Studies, edited byLynne Bowker, Michael Cronin, Dorothy Kenny, and Jennifer Pearson, 123–36. Manchester: St. Jerome Publishing.
    [Google Scholar]
28. Jones, Ruth, and Ann Irvine
    2013 “The (Un)Faithful Machine Translator.” InProceedings of the 7th Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities, Sofia, Bulgaria, 96–101. Stroudsburg: Association for Computational Linguistics, www.aclweb.org/anthology/W13-2700
    [Google Scholar]
29. Kelly, Nataly
    2014 “Why So Many Translators Hate Translation Technology.” Huffington Post. The Blog. https://www.huffingtonpost.com/nataly-kelly/why-so-many-translators-h_b_5506533.html
    [Google Scholar]
30. 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 Linguistics108: 121–132. 10.1515/pralin‑2017‑0014
    https://doi.org/10.1515/pralin-2017-0014 [Google Scholar]
31. Koehn, P., and R. Knowles
    2017 “Six Challenges for Neural Machine Translation.” InProceedings of the First Workshop on Neural Machine Translation, Vancouver, BC, Canada, 28–39. www.aclweb.org/anthology/W17-3204. 10.18653/v1/W17‑3204
    https://doi.org/10.18653/v1/W17-3204 [Google Scholar]
32. Koponen, Maarit
    2016 “Is Post-Editing Worth the Effort? A Survey of Research into Post-Editing and Effort.” JosTrans: Journal of Specialised Translation25: 131–148.
    [Google Scholar]
33. Krings, Hans P.
    2001 Repairing Texts: Empirical Investigations of Machine Translation Post-Editing Processes. Ohio: Kent State University Press.
    [Google Scholar]
34. Lacruz, Isabel, and Gregory M. Shreve
    2014 “Pauses and Cognitive Effort in Post-Editing.” InPost-Editing of Machine Translation: Processes and Applications, edited bySharon O’Brien, Laura Winther Balling, Michael Carl, Michel Simard, and Lucia Specia, 287–314. Newcastle-Upon-Tyne: Cambridge Scholars.
    [Google Scholar]
35. 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 Research5 (2): 1–13. 10.12807/ti.105202.2013.a01
    https://doi.org/10.12807/ti.105202.2013.a01 [Google Scholar]
36. 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.
37. 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 Revista53: 193–196.
    [Google Scholar]
38. Moorkens, Joss
    2017 “Under Pressure: Translation in Times of Austerity.” Perspectives25 (3): 464–477. 10.1080/0907676X.2017.1285331
    https://doi.org/10.1080/0907676X.2017.1285331 [Google Scholar]
39. Moorkens, Joss, and Sharon O’Brien
    2015 “Post-Editing Evaluations: Trade-offs between Novice and Professional Participants.” InProceedings of the 18th Annual Conference of the European Association for Machine Translation (EAMT 2015), edited byİIknur Durgar El-Kahlout, Mehmed Özkan, Felipe Sánchez-Martínez, Gema Ramírez-Sánchez, Fred Hollowood, and Andy Way, 75–81.
    [Google Scholar]
40. Moorkens, Joss, and Sharon O’Brien
    2017 “Assessing User Interface Needs of Post-Editors of Machine Translation.” InHuman Issues in Translation Technology: The IATIS Yearbook, edited byDorothy Kenny, 109–130, Oxford, United Kingdom: Routledge.
    [Google Scholar]
41. 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 Translation29 (3–4): 267–284. 10.1007/s10590‑015‑9175‑2
    https://doi.org/10.1007/s10590-015-9175-2 [Google Scholar]
42. Nida, Eugene
    1964 Towards a Science of Translating. Leiden: Brill.
    [Google Scholar]
43. Nitzke, Jean
    2016 “Monolingual Post-Editing: An Exploratory Study on Research Behaviour and Target Text Quality.” InEye-tracking and Applied Linguistics, edited bySilvia Hansen-Schirra, and Sambor Grucza, 83–109. Berlin: Language Science Press. 10.17169/langsci.b108.236
    https://doi.org/10.17169/langsci.b108.236 [Google Scholar]
44. PACTE group
    PACTE group 2005 “Investigating Translation Competence: Conceptual and Methodological Issues.” Meta50 (2): 609–619. 10.7202/011004ar
    https://doi.org/10.7202/011004ar [Google Scholar]
45. 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 Linguistics93: 7–16. 10.2478/v10108‑010‑0010‑x
    https://doi.org/10.2478/v10108-010-0010-x [Google Scholar]
46. Pym, Anthony
    2008 “Professional Corpora: Teaching Strategies for Work with Online Documentation, Translation Memories and Content Management.” Chinese Translator’s Journal29 (2): 41–45.
    [Google Scholar]
47. Reiss, Katharina
    1981 “Type, Kind and Individuality of Text: Decision Making in Translation.” Poetics Today2 (4): 121–131. 10.2307/1772491
    https://doi.org/10.2307/1772491 [Google Scholar]
48. Sennrich, Rico, Barry Haddow, and Alexandra Birch
    2016a “Improving Neural Machine Translation Models with Monolingual Data.” InProceedings of the 54th Annual Meeting of the Association for Computational Linguistics from August 7 to August 12, 2016, 86–96. Berlin, Germany.
    [Google Scholar]
49. Sennrich, Rico, Barry Haddow, and Alexandra Birch
    2016b “Neural Machine Translation of Rare Words with Subword Units.” InProceedings of the 54th Annual Meeting of the Association for Computational Linguistics from August 7 to August 12, 2016, 1715–1725. Berlin, Germany.
    [Google Scholar]
50. Sennrich, Rico, Barry Haddow, and Alexandra Birch
    2016c “Controlling Politeness in Neural Machine Translation via Side Constraints.” InProceedings of NAACL-HLT 2016, 35–40.
    [Google Scholar]
51. 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.” InProceedings of the Software Demonstrations from the 15th Conference of the European Chapter of the Association for Computational Linguistics, 65–68. aclweb.org/anthology/E17-3000. 10.18653/v1/E17‑3017
    https://doi.org/10.18653/v1/E17-3017 [Google Scholar]
52. Somers, Harold
    2001 Computers and Translation: A Translator’s Guide. Amsterdam: John Benjamins. 10.1075/btl.35
    https://doi.org/10.1075/btl.35 [Google Scholar]
53. 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]
54. Specia, Lucia
    2011 “Exploiting Objective Annotations for Measuring Translation Post-Editing Effort.” In Proceedings of the 15th Conference of the European Association forMachine Translation, 73–80. Leuven, Belgium.
    [Google Scholar]
55. Specia, Lucia, and Kashif Shah
    2018 “Machine Translation Quality Estimation: Applications and Future Perspectives.” InTranslation Quality Assessment: From Principles to Practice, edited byJoss Moorkens, Sheila Castilho, Federico Gaspari, and Stephen Doherty, 201–236. Heidelberg: Springer International Publishing. 10.1007/978‑3‑319‑91241‑7_10
    https://doi.org/10.1007/978-3-319-91241-7_10 [Google Scholar]
56. 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. byCecilia Alvstad, Annjo K. Greenall, Hanne Jansen, and Kristiina Taivalkoski-Shilov. doi:  10.1080/0907676X.2018.1520907
    https://doi.org/10.1080/0907676X.2018.1520907 [Google Scholar]
57. Teixeira, Carlos S. C.
    2014 “Perceived vs. Measured Performance in the Post-Editing of Suggestions from Machine Translation and Translation Memories.” InProceedings of the Third Workshop on Post-Editing Technology and Practice (WPTP-3), edited bySharon O’Brien, Michel Simard, and Lucia Specia, 45–59.
    [Google Scholar]
58. Thouin, Benoît
    1982 “The METEO System.” InPractical Experience of Machine Translation: Proceedings of Translating and the Computer 1981, edited byVeronica Lawson, 39–44, Amsterdam: North-Holland Publishing.
    [Google Scholar]
59. Toral, Antonio, and Andy Way
    2015 “Translating Literary Text between Related Languages using SMT.” InProceedings of NAACL-HLT Fourth Workshop on Computational Linguistics for Literature, 123–132. Denver, Colorado. 10.3115/v1/W15‑0714
    https://doi.org/10.3115/v1/W15-0714 [Google Scholar]
60. Toral, Antonio, and Andy Way
    2018 “What level of quality can Neural Machine Translation attain on literary text?” InTranslation Quality Assessment: From Principles to Practice, edited byJoss Moorkens, Sheila Castilho, Federico Gaspari, and Stephen Doherty, 263–287. Heidelberg: Springer International Publishing. 10.1007/978‑3‑319‑91241‑7_12
    https://doi.org/10.1007/978-3-319-91241-7_12 [Google Scholar]
61. Toral, Antonio, and Victor M. Sánchez-Cartagena
    2017 “A Multifaceted Evaluation of Neural versus Phrase-Based Machine Translation for 9 Language Directions.” InConference of the European Chapter of the Association for Computational Linguistics, EACL 2017. Valencia, Spain. 10.18653/v1/E17‑1100
    https://doi.org/10.18653/v1/E17-1100 [Google Scholar]
62. Toral, Antonio, Martijn Wieling, and Andy Way
    2018 “Post-editing Effort of a Novel with Statistical and Neural Machine Translation.” Frontiers in Digital Humanities5:9. 10.3389/fdigh.2018.00009
    https://doi.org/10.3389/fdigh.2018.00009 [Google Scholar]
63. Vasconcellos, Muriel
    1985 “Machine Aids to Translation: A Holistic Scenario for Maximizing the Technology.” InOvercoming 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 byHumphrey Tonkin, and Karen Johnson-Weiner, 27–34. New York: Center for Research and Documentation on World Problems.
    [Google Scholar]
64. Vaswani, Ashish, Yinggong Zhao, Victoria Fossum and David Chiang
    2013 “Decoding with Large-Scale Neural Language Models Improves Translation.” InProceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, Seattle, Washington, 1387–1392. Stroudsburg: Association for Computational Linguistics.
    [Google Scholar]
65. Viera, Lucas Nunes
    2014 “Indices of cognitive effort in machine translation post-editing.” Machine Translation28 (3–4):187–216. 10.1007/s10590‑014‑9156‑x
    https://doi.org/10.1007/s10590-014-9156-x [Google Scholar]
66. Wagner, Elizabeth
    1985 “Post-Editing Systran-A Challenge for Commission Translators.” Terminologie et Traduction3: 1–7.
    [Google Scholar]
67. Way, Andy
    2013 “Traditional and Emerging Use-Cases for Machine Translation.” InProceedings of Translating and the Computer35. London, United Kingdom.
    [Google Scholar]
68. Way, Andy
    2018a “Quality Expectations of Machine Translation.” InTranslation Quality Assessment: From Principles to Practice, edited byJoss Moorkens, Sheila Castilho, Federico Gaspari, and Stephen Doherty, 159–178. Heidelberg: Springer International Publishing. 10.1007/978‑3‑319‑91241‑7_8
    https://doi.org/10.1007/978-3-319-91241-7_8 [Google Scholar]
69. Way, Andy
    2018b “Machine Translation: Where We Are at Today.” InThe Bloomsbury Companion to Language Industry Studies, edited byErik Angelone, Gary Massey, and Maureen Ehrensberger-Dow. London: Bloomsbury.
    [Google Scholar]
70. 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, https://arxiv.org/abs/1609.08144