MT error detection and correction by Chinese language learners

References

1. Baayen, R. H., D. J. Davidson, and D. M. Bates
   2008 “Mixed-effects modeling with crossed random effects for subjects and items.” Journal of Memory and Language59(4): 390–412. 10.1016/j.jml.2007.12.005
   https://doi.org/10.1016/j.jml.2007.12.005 [Google Scholar]
2. Bates, Douglas, Martin Mächler, Ben Bolker, and Steve Walker
   2015 “Fitting linear mixed-effects models using Lme4.” Journal of Statistical Software671: 1–48. 10.18637/jss.v067.i01
   https://doi.org/10.18637/jss.v067.i01 [Google Scholar]
3. Bellassen, J., and Pengpeng Zhang
   1997 A Key to Chinese Speech and Writing. Beijing: Sinolingua.
   [Google Scholar]
4. Bowker, Lynne, and Jairo Buitrago Ciro
   2019 Machine Translation and Global Research: Towards Improved Machine Translation Literacy in the Scholarly Community. Bingley, UK: Emerald. 10.1108/9781787567214
   https://doi.org/10.1108/9781787567214 [Google Scholar]
5. Chang, Chia-chien, and Masaru Yamada
   2021 “Translation tasks for learning collocations: Effects of machine translation plus post-editing and sight translation.” English Teaching & Learning45(1): 27–44. 10.1007/s42321‑020‑00059‑x
   https://doi.org/10.1007/s42321-020-00059-x [Google Scholar]
6. Chang, Pi-Chuan, Michel Galley, and Christopher D. Manning
   2008 “Optimizing Chinese word segmentation for machine translation performance.” InProceedings of the Third Workshop on Statistical Machine Translation – StatMT ’08, 224–32. Columbus, OH: Association for Computational Linguistics. 10.3115/1626394.1626430
   https://doi.org/10.3115/1626394.1626430 [Google Scholar]
7. Chen, Hsin-hsi
   1994 “Contextual analysis of Chinese sentences with punctuation marks.” Literary and Linguistic Computing9(4): 281–89. 10.1093/llc/9.4.281
   https://doi.org/10.1093/llc/9.4.281 [Google Scholar]
8. Clifford, Joan, Lisa Merschel, and Joan Munné
   2013 “Surveying the landscape – What is the role of machine translation in language learning.” @tic. Revista d’innovació Educativa101: 108–21.
   [Google Scholar]
9. Cohen, Jacob
   1988 Statistical Power Analysis for the Behavioral Sciences. 2nd ed.New York: Routledge. 10.4324/9780203771587
   https://doi.org/10.4324/9780203771587 [Google Scholar]
10. Correa, Maite
    2014 “Leaving the “peer” out of peer-editing: Online translators as a pedagogical tool in the Spanish as a second language classroom.” Latin American Journal of Content and Language Integrated Learning7(1): 1–20. 10.5294/laclil.2014.7.1.1
    https://doi.org/10.5294/laclil.2014.7.1.1 [Google Scholar]
11. Cui, Songren, and Kuo-ming Sung
    2022 “Directional complement.” InA Reference Grammar for Teaching Chinese: Syntax and Discourse, ed. bySongren Cui and Kuo-ming Sung, 447–89. Singapore: Springer. 10.1007/978‑981‑33‑4207‑1_11
    https://doi.org/10.1007/978-981-33-4207-1_11 [Google Scholar]
12. Depraetere, Ilse
    2010 “What counts as useful advice in a university post-editing training context? Report on a case study.” InProceedings of the 14th Annual Conference of the European Association for Machine Translation. St Raphaël, France. https://halshs.archives-ouvertes.fr/halshs-00603177
    [Google Scholar]
13. Dorst, Aletta G., Susana Valdez, and Heather Bouman
    2022 “Machine translation in the multilingual classroom: How, when and why do humanities students at a Dutch University use machine translation?’ Translation and Translanguaging in Multilingual Contexts8(1): 49–66. 10.1075/ttmc.00080.dor
    https://doi.org/10.1075/ttmc.00080.dor [Google Scholar]
14. ELIS Research
    ELIS Research 2023 “European Language Industry Survey 2023: Trends, Expectations and Concerns of the European Language Industry.” 10. ELIS. ELIA, EMT, EUATC, FIT Europe, GALA, LIND, Women in Localization. https://elis-survey.org/wp-content/uploads/2023/03/ELIS-2023-report.pdf
    [Google Scholar]
15. Fonseca, Norma, and Fabio Alves
    2016 “Assessing complexity and difficulty levels of machine-translated texts.” Letras & Letras32(1): 306–337. 10.14393/LL63‑v32n1a2016‑16
    https://doi.org/10.14393/LL63-v32n1a2016-16 [Google Scholar]
16. García, Ignacio, and María Isabel Pena
    2011 “Machine translation-assisted language learning: Writing for beginners.” Computer Assisted Language Learning24(5): 471–87. 10.1080/09588221.2011.582687
    https://doi.org/10.1080/09588221.2011.582687 [Google Scholar]
17. Groves, Mike, and Klaus Mundt
    2021 “A ghostwriter in the machine? Attitudes of academic staff towards machine translation use in internationalised higher education.” Journal of English for Academic Purposes501: article 100957. 10.1016/j.jeap.2021.100957
    https://doi.org/10.1016/j.jeap.2021.100957 [Google Scholar]
18. Guerberof-Arenas, Ana, and Joss Moorkens
    2019 “Machine translation and post-editing training as part of a master’s programme.” The Journal of Specialised Translation311: 217–38.
    [Google Scholar]
19. Hu, Ke, and Patrick Cadwell
    2016 “A comparative study of post-editing guidelines.” Baltic Journal of Modern Computing4 (2): 346–53.
    [Google Scholar]
20. ISO 18587
    ISO 18587 2017 Translation Services – Post-Editing of Machine Translation Output – Requirements. Geneva: ISO.
    [Google Scholar]
21. Jensen, Kristian T. H.
    2009 “Indicators of text complexity.” InBehind the Mind: Methods, Models and Results in Translation Process Research, ed. bySusanne Göpferich, Arnt Lykke Jakobsen, and Inger M. Mees, 61–80. Copenhagen: Samfundslitteratur.
    [Google Scholar]
22. Jia, Yanfang, Michael Carl, and Xiangling Wang
    2019a “How does the post-editing of neural machine translation compare with from-scratch translation.” The Journal of Specialised Translation311: 60–86.
    [Google Scholar]
23. Jia, Yanfang, Michael Carl, and Xiangling Wang
    2019b “Post-editing neural machine translation versus phrase-based machine translation for English–Chinese.” Machine Translation33(1–2): 9–29. 10.1007/s10590‑019‑09229‑6
    https://doi.org/10.1007/s10590-019-09229-6 [Google Scholar]
24. Jia, Yanfang, and Binghan Zheng
    2022 “The interaction effect between source text complexity and machine translation quality on the task difficulty of NMT post-editing from English to Chinese: A multi-method study.” Across Languages and Cultures23(1): 36–55. 10.1556/084.2022.00120
    https://doi.org/10.1556/084.2022.00120 [Google Scholar]
25. Klimova, Blanka, Marcel Pikhart, Alice Delorme Benites, Caroline Lehr, and Christina Sanchez-Stockhammer
    2023 “Neural machine translation in foreign language teaching and learning: A systematic review.” Education and Information Technologies281: 663–682. 10.1007/s10639‑022‑11194‑2
    https://doi.org/10.1007/s10639-022-11194-2 [Google Scholar]
26. Läubli, Samuel, Chantal Amrhein, Patrick Düggelin, Beatriz Gonzalez, Alena Zwahlen, and Martin Volk
    2019 “Post-editing productivity with neural machine translation: An empirical assessment of speed and quality in the banking and finance domain.” arXiv. arxiv.org/abs/1906.01685
    [Google Scholar]
27. Li, Xiaoshi
    2010 “Sociolinguistic variation in the speech of learners of Chinese as a second language.” Language Learning60(2): 366–408. 10.1111/j.1467‑9922.2009.00560.x
    https://doi.org/10.1111/j.1467-9922.2009.00560.x [Google Scholar]
28. Liu, Baolin, Zhongning Wang, and Zhixing Jin
    2010 “The effects of punctuations in Chinese sentence comprehension: An ERP study.” Journal of Neurolinguistics23(1): 66–80. 10.1016/j.jneuroling.2009.08.004
    https://doi.org/10.1016/j.jneuroling.2009.08.004 [Google Scholar]
29. Lommel, Arle
    2018 “Metrics for translation quality assessment: A case for standardising error typologies.” InTranslation Quality Assessment: From Principles to Practice, ed. byJoss Moorkens, Sheila Castilho, Federico Gaspari, and Stephen Doherty, 109–27. Cham: Springer International. 10.1007/978‑3‑319‑91241‑7_6
    https://doi.org/10.1007/978-3-319-91241-7_6 [Google Scholar]
30. Mellinger, Christopher D.
    2017 “Translators and machine translation: Knowledge and skills gaps in translator pedagogy.” The Interpreter and Translator Trainer11(4): 280–93. 10.1080/1750399X.2017.1359760
    https://doi.org/10.1080/1750399X.2017.1359760 [Google Scholar]
31. Mellinger, Christopher D., and Thomas A. Hanson
    2017 Quantitative Research Methods in Translation and Interpreting Studies. London: Routledge.
    [Google Scholar]
32. Miyata, Rei, and Atsushi Fujita
    2021 “Understanding pre-editing for black-box neural machine translation.” InProceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, 1539–50. EACL. 10.18653/v1/2021.eacl‑main.132
    https://doi.org/10.18653/v1/2021.eacl-main.132 [Google Scholar]
33. Moorkens, Joss
    2018 “What to expect from neural machine translation: A practical in-class translation evaluation exercise.” The Interpreter and Translator Trainer12(4): 375–87. 10.1080/1750399X.2018.1501639
    https://doi.org/10.1080/1750399X.2018.1501639 [Google Scholar]
34. Niño, Ana
    2008 “Evaluating the use of machine translation post-editing in the foreign language class.” Computer Assisted Language Learning21(1): 29–49. 10.1080/09588220701865482
    https://doi.org/10.1080/09588220701865482 [Google Scholar]
35. Niño, Ana
    2009 “Machine translation in foreign language learning: Language learners’ and tutors’ perceptions of its advantages and disadvantages.” ReCALL21(2): 241–58. 10.1017/S0958344009000172
    https://doi.org/10.1017/S0958344009000172 [Google Scholar]
36. Niño, Ana
    2020 “Exploring the use of online machine translation for independent language learning.” Research in Learning Technology281: article 2402. 10.25304/rlt.v28.2402
    https://doi.org/10.25304/rlt.v28.2402 [Google Scholar]
37. O’Brien, Sharon
    2021 “Post-editing.” InHandbook of Translation Studies, vol.51, ed. byYves Gambier and Luc van Doorslaer, 178–84. Amsterdam: John Benjamins. 10.1075/hts.5.pos3
    https://doi.org/10.1075/hts.5.pos3 [Google Scholar]
38. O’Brien, Sharon
    2022 “How to deal with errors in machine translation: Post-editing.” InMachine Translation for Everyone: Empowering Users in the Age of Artificial Intelligence, ed. byDorothy Kenny, 105–20. Berlin: Language Science Press.
    [Google Scholar]
39. 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 Linguistics931: 7–16. 10.2478/v10108‑010‑0010‑x
    https://doi.org/10.2478/v10108-010-0010-x [Google Scholar]
40. R Core Team
    R Core Team 2023 “R: The R Project for Statistical Computing” 2023 https://www.r-project.org/
41. Rico Pérez, Celia
    2024 “Re-thinking machine translation post-editing guidelines.” The Journal of Specialised Translation411: 26–47. 10.26034/cm.jostrans.2024.4696
    https://doi.org/10.26034/cm.jostrans.2024.4696 [Google Scholar]
42. Rico, Celia, and Diana González Pastor
    2022 “The role of machine translation in translation education: A thematic analysis of translator educators’ beliefs.” Translation & Interpreting14(1): 177–197. 10.12807/ti.114201.2022.a010
    https://doi.org/10.12807/ti.114201.2022.a010 [Google Scholar]
43. Sánchez-Gijón, Pilar, and Olga Torres-Hostench
    2014 “MT post-editing into the mother tongue or into a foreign language? Spanish-to-English MT translation output post-edited by translation trainees.” InProceedings of the 11th Conference of the Association for Machine Translation in the Americas, 5–19. Vancouver, Canada: AMTA.
    [Google Scholar]
44. Screen, Ben
    2017 “Machine translation and Welsh: Analysing free statistical machine translation for the professional.” The Journal of Specialised Translation281: 317–44.
    [Google Scholar]
45. Somers, H. L.
    2000 “Machine translation in the classroom.” InComputers and Translation: A Translator’s Guide, ed. byH. L. Sommers, 319–340. Amsterdam: John Benjamins. 10.1075/btl.35.20som
    https://doi.org/10.1075/btl.35.20som [Google Scholar]
46. Sun, Chaofen
    2006 Chinese: A Linguistic Introduction. Cambridge: Cambridge University Press. 10.1017/CBO9780511755019
    https://doi.org/10.1017/CBO9780511755019 [Google Scholar]
47. TAUS, and CNGL
    TAUS, and CNGL 2010 “Machine translation postediting guidelines.” taus-website-media.s3.amazonaws.com/images/stories/guidelines/taus-cngl-machine-translation-postediting-guidelines.pdf
48. 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, ed. byJoss Moorkens, Sheila Castilho, Federico Gaspari, and Stephen Doherty, 263–87. Cham: Springer International. 10.1007/978‑3‑319‑91241‑7_12
    https://doi.org/10.1007/978-3-319-91241-7_12 [Google Scholar]
49. Vardaro, Jennifer, Moritz Schaeffer, and Silvia Hansen-Schirra
    2019 “Translation quality and error recognition in professional neural machine translation post-editing.” Informatics6(3): article 41. 10.3390/informatics6030041
    https://doi.org/10.3390/informatics6030041 [Google Scholar]
50. Wang, Xiangling, Tingting Wang, Ricardo Muñoz Martín, and Yanfang Jia
    2021 “Investigating usability in postediting neural machine translation: Evidence from translation trainees’ self-perception and performance.” Across Languages and Cultures22(1): 100–123. 10.1556/084.2021.00006
    https://doi.org/10.1556/084.2021.00006 [Google Scholar]
51. Wang, Yining, Long Zhou, Jiajun Zhang, and Chengqing Zong
    2017 “Word, subword or character? An empirical study of granularity in Chinese-English NMT.” InMachine Translation, ed. byDerek F. Wong and Deyi Xiong, 30–42. Singapore: Springer. 10.1007/978‑981‑10‑7134‑8_4
    https://doi.org/10.1007/978-981-10-7134-8_4 [Google Scholar]
52. Yamada, Masaru
    2019 “The impact of google neural machine translation on post-editing by student translators.” The Journal of Specialised Translation311: 81–106.
    [Google Scholar]
53. Yang, Jizhou
    ed. 2016 Chinese Course. 3rd ed. Beijing: Beijing Language and Culture University Press.
    [Google Scholar]
54. Zhang, Hong, and Olga Torres-Hostench
    2022 “Training in machine translation post-editing for foreign language students.” Language Learning & Technology26(1): 1–17.
    [Google Scholar]
55. Zhang, Qi, and Zhouxiang Lu
    2014 “The writing of Chinese characters by CFL learners: Can writing on Facebook and using machine translation help?’ Language Learning in Higher Education4(2): 441–467. 10.1515/cercles‑2014‑0023
    https://doi.org/10.1515/cercles-2014-0023 [Google Scholar]
56. Zhen, Yu-Yao, Ya-Ning Wu, Guang-Ming Yu, and Chun-Ping Zheng
    2021 “A review study of the application of machine translation in education from 2011 to 2020.” InProceedings of the 29th International Conference on Computers in Education, ed. byMaria Mercedes T. Rodrigo, Sridhar Iyer, and Antonija Mitrovic, 17–24. Bangkok, Thailand: Asia-Pacific Society for Computers in Education (APSCE).
    [Google Scholar]