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Abstract

Abstract

This article addresses the issue of using AI-generated translations to perform contrastive analysis. The aim is to establish whether bidirectional translation corpora (and by extension human translators) have become superfluous, given the success of AI-generated translations. To investigate this, results from a previous study of English and Norwegian based on bidirectional data are compared with results from a study based on AI-generated data. The AI-generated translations show a markedly higher Mutual Correspondence between the verbs than the human translations. This AI-translation effect may give an inaccurate picture of how equivalent the verbs really are. In cases where AI and human translations deviate, the latter are characterised by semantically more specific verbs. The study highlights some features only available in bidirectional corpora, including the possibility of taking individual variation into account. The findings suggest that bidirectional corpora (and human translators) still have a role to play in contrastive studies.

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2024-09-20
2024-10-06
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References

  1. Ahrenberg, L.
    2017 Comparing Machine Translation and Human Translation: A Case Study. Proceedings of the Workshop Human-Informed Translation and Interpreting Technology (HiT-IT). Varna, Bulgaria, 7 September 2017. Association for Computational Linguistics. –. 10.26615/978‑954‑452‑042‑7_003
    https://doi.org/10.26615/978-954-452-042-7_003 [Google Scholar]
  2. Altenberg, B.
    1999 Adverbial connectors in English and Swedish: Semantic and lexical correspondences. InOut of Corpora: Studies in Honour of Stig Johansson, H. Hasselgård and S. Oksefjell (eds), –. Amsterdam/Atlanta: Rodopi. 10.1163/9789004653689_022
    https://doi.org/10.1163/9789004653689_022 [Google Scholar]
  3. Baker, M.
    1993 Corpus linguistics and translation studies. Implications and applications. InText and Technology: In Honour of John Sinclair, M. Baker, G. Francis and E. Tognini-Bonelli (eds), –. Amsterdam/Philadelphia: John Benjamins Publishing Company. 10.1075/z.64.15bak
    https://doi.org/10.1075/z.64.15bak [Google Scholar]
  4. 2004 A corpus-based view of similarity and difference in translation. International Journal of Corpus Linguistics():–. 10.1075/ijcl.9.2.02bak
    https://doi.org/10.1075/ijcl.9.2.02bak [Google Scholar]
  5. Bjorvand, H. and Lindeman, F. O.
    2007Våre arveord. Etymologisk ordbok. Oslo: Novus.
    [Google Scholar]
  6. Bokmålsordboka
    Bokmålsordboka 2024 The Language Council of Norway and the University of Bergen. Available athttps://ordbokene.no/ [last accessed15 January 2024].
  7. Brahmbhatt, A.
    2023 GPT-3.5 vs GPT-4: an in-depth analysis of OpenAI’s language models. Available athttps://auberginesolutions.com/blog/gpt-3-5-vs-gpt-4-an-in-depth-analysis-of-openais-language-models/ [last accessed5 April 2024].
  8. Brown, P. F., Della Pietra, S. A., Della Pietra, V. J. and Mercer, R. L.
    1990 The mathematics of Statistical Machine Translation: Parameter Estimation. Computational Linguistics(), –.
    [Google Scholar]
  9. Cambridge Dictionary
    Cambridge Dictionary 2024English Dictionary Online. Cambridge University Press. Available athttps://dictionary.cambridge.org/dictionary/english/ [last accessed15 January 2024].
    [Google Scholar]
  10. Ebeling, J.
    1998 The Translation Corpus Explorer: A browser for parallel texts. InCorpora and Cross-linguistic Research: Theory, Method and Case Studies, S. Johansson and S. Oksefjell (eds), –. Amsterdam: Rodopi. 10.1163/9789004653665_008
    https://doi.org/10.1163/9789004653665_008 [Google Scholar]
  11. Ebeling, J. and Ebeling, S. O.
    2015 An English-Norwegian contrastive analysis of downtoners, more or less. Nordic Journal of English Studies (NJES)(): –. 10.35360/njes.340
    https://doi.org/10.35360/njes.340 [Google Scholar]
  12. Ebeling, S. O.
    2017 Bringing home the bacon! A contrastive study of the cognates bring/bringe in English and Norwegian. Kalbotyra: –. 10.15388/Klbt.2017.11193
    https://doi.org/10.15388/Klbt.2017.11193 [Google Scholar]
  13. Gellerstam, M.
    1986 Translationese in Swedish novels translated from English. InTranslation Studies in Scandinavia, L. Wollin and H. Linquist (eds), –. Lund: CWK Gleerup.
    [Google Scholar]
  14. Hasselgård, H.
    2010 Contrastive analysis / contrastive linguistics. InThe Routledge Linguistics Encyclopedia, Third Edition, K. Malmkjær (ed.), –. London: Routledge.
    [Google Scholar]
  15. Hendy, A., Abdelrehim, M., Sharaf, A., Raunak, V., Gabr, M., Matsushita, H., Kim, Y. J., Afify, M. and Awadalla, H.
    2023 How good are GPT models at machine translation? A comprehensive evaluation. 10.48550/arXiv.2302.09210Available athttps://arxiv.org/pdf/2302.09210.pdf [last accessed8 April 2024].
    https://doi.org/10.48550/arXiv.2302.09210
  16. House, J.
    2023Translation: The Basics, 2nd ed. London: Routledge. 10.4324/9781003355823
    https://doi.org/10.4324/9781003355823 [Google Scholar]
  17. Hutchins, W. J.
    2001 Machine translation over fifty years. Histoire Épistémologie Langage(), –. 10.3406/hel.2001.2815
    https://doi.org/10.3406/hel.2001.2815 [Google Scholar]
  18. Johansson, S.
    2007Seeing Through Multilingual Corpora: On the Use of Corpora in Contrastive Studies. Amsterdam/Philadelphia: John Benjamins. 10.1075/scl.26
    https://doi.org/10.1075/scl.26 [Google Scholar]
  19. Kanade, V.
    2023 What is ChatGPT? Characteristics, uses, and alternatives. Available athttps://www.spiceworks.com/tech/artificial-intelligence/articles/what-is-chatgpt/ [last accessed4 April 2024].
  20. Klaudy, K. and Károly, K.
    2005 Implicitation in translation: Empirical evidence for operational asymmetry in translation. Across Languages and Cultures(), –. 10.1556/Acr.6.2005.1.2
    https://doi.org/10.1556/Acr.6.2005.1.2 [Google Scholar]
  21. Koehn, P.
    2020Neural Machine Translation. Cambridge: Cambridge University Press. 10.1017/9781108608480
    https://doi.org/10.1017/9781108608480 [Google Scholar]
  22. Mandal, S. A.
    2019 Evolution of Machine Translation. Towards Data Science. Available athttps://medium.com/towards-data-science/evolution-of-machine-translation-5524f1c88b25 [last accessed13 July 2024].
    [Google Scholar]
  23. Marr, B.
    2023 The Top 10 Limitations of ChatGPT. Available athttps://www.forbes.com/sites/bernardmarr/2023/03/03/the-top-10-limitations-of-chatgpt/?sh=733a9e6a8f35 [last accessed5 April 2024].
  24. Muftah, M.
    2022 Machine vs. human translation: A new reality or a threat to professional Arabic-English translators. PSU Research Review, Emerald Publishing Limited. Available athttps://www.emerald.com/insight/content/doi/10.1108/PRR-02-2022-0024/full/pdf?title=machine-vs-human-translation-a-new-reality-or-a-threat-to-professional-arabic-english-translators [last accessed5 April 2024].
  25. NAOB
    NAOB 2024Det Norske Akademis Ordbok. Det Norske Akademi for Språk og Litteratur. Available athttps://naob.no/ [last accessed15 January 2024].
    [Google Scholar]
  26. OED
    OED 2017 / 2024Oxford English Dictionary. Oxford University Press. Available athttps://www.oed.com/ [accessed25 July 2017 / 15 January 2024].
    [Google Scholar]
  27. Quirk, R., Greenbaum, S., Leech, G. and Svartvik, J.
    1985A Comprehensive Grammar of the English Language. London: Longman.
    [Google Scholar]
  28. Sinclair, J.
    1999 A way with common words. InOut of Corpora: Studies in Honour of Stig Johansson, H. Hasselgård and S. Oksefjell (eds), –. Amsterdam: Rodopi. 10.1163/9789004653689_016
    https://doi.org/10.1163/9789004653689_016 [Google Scholar]
  29. Summa Linguæ
    Summa Linguæ 2021 Rule-Based vs. Statistical vs. Neural Machine Translation. Available athttps://summalinguae.com/language-technology/rule-based-machine-translation-vs-statistical-and-neural-machine-translation/ [last accessed4 April 2024].
  30. Vanmassenhove, E., Shterionov, D. and Gwilliam, M.
    2021 Machine translationese: Effects of algorithmic bias on linguistic complexity in machine translation. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, –. Available athttps://aclanthology.org/2021.eacl-main.188.pdf [last accessed4 April 2024]. 10.18653/v1/2021.eacl‑main.188
    https://doi.org/10.18653/v1/2021.eacl-main.188 [Google Scholar]
  31. Wikipedia: The Free Encyclopedia
    Wikipedia: The Free Encyclopedia 2024 GPT-4. Available athttps://en.wikipedia.org/wiki/GPT-4 [last accessed13 July 2024].
  32. English-Norwegian Parallel Corpus
    English-Norwegian Parallel Corpus (1994–1997), Dept. of British and American Studies, University of Oslo. Compiled byStig Johansson (project leader), Knut Hofland (project leader), Jarle Ebeling (research assistant), Signe Oksefjell (research assistant). Available athttps://www.hf.uio.no/ilos/english/services/knowledge-resources/omc/enpc/ [last accessed15 January 2024].
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  • Article Type: Research Article
Keywords: cognates ; Mutual Correspondence ; AI-generated translations ; English/Norwegian ; GPTese
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