Equivalence Levels of Literary Corpus Translation Using a Freeware Analysis Toolkit

Frans Sayogie, Moh. Supardi


Machine translation has the potential to make huge contributions to translation industries, but it seems, for now, that machine translation equivalence has led to a crucial point for literary translation by using machine translation because of the problem of the equivalence itself. This paper, therefore, aimed to see the equivalence degree of literary translation resulted by machine translation, i.e., freeware toolkit AntConc 3.5.0 software 2019. The data were collected from English-Indonesian J.K. Rowling's Harry Potter and The Order of The Phoenix novel by using the software to find the equivalent translation. The collected data were analyzed qualitatively based on the strategy of translation equivalent level proposed by Mona Baker (1992). The analyzed data revealed that the equivalent level of the software mostly occurred in a word level, above word level, and grammatical level. The software was likely difficult to find a textual level and a pragmatic level of translation equivalence because they required a context and still needed human involvement as part of a greater creative project of translation which were not done by the machine translation. After all, Antconc 3.5.0 as Computer Assisted Tool (CAT) brought a huge contribution to translation industry, helped to analyze large corpora particularly to find the degree of translation equivalence in word and above word level.


CAT tool; equivalence level; literary translation


Anh, T. V. (2018). Strategies For Non-Equivalence At Word Level In Literary Translation – A Case Study. 11.

Arcan, M. (2018). A comparison of statistical and neural machine translation for slovene, serbian and croatian. 3–10.

Baker, M. (2011). In other words: A coursebook on translation (2nd ed). Routledge.

Bowker, L., & Pearson, L. (2002). Working with specialized language: A practical guide to using corpora. Routledge. Journal.

Cadwell, P. (2017). Resistance and accommodation: Factors for the (non-) adoption of machine translation among professional translators. https://doi.org/doi/full/10.1080/0907676X.2017.1337210

De Sutter, G. et. al. (2017). Towards a corpus-based, statistical approach to translation quality: Measuring and visualizing linguistic deviance in student translation. 16, 25–39.

Doherty, S., & Dorothy, K. (2014). The design and evaluation of a statistical machine translation syllabus for translation students. 8(2), 295–315.

Ehrensberger‐Dow, M. (2017). An Ergonomic Perspective of Translation. In The Handbook of Translation and Cognition (pp. 332–349). John Wiley & Sons, Ltd. https://doi.org/10.1002/9781119241485.ch18

Evert, S., & Neumann, S. (2017). The impact of translation direction on characteristics of translated texts: A multivariate analysis for English and German.In G. De Sutter, I. Delaere, & M.-A. Lefer (Eds.), Empirical Translation Studies: New theoretical and methodological traditions. 47–80.

Ezzati, A. (2016). Non-Equivalence at Grammatical and Word Level and the Strategies to Deal with: A Case Study of English Translation into Persian. International Journal of Language and Linguistics, 3(3), 7.

Flanagan, M. (2016). Cause for concern? Attitudes towards translation crowdsourcing in professional translators’ blogs. The Journal of Specialised Translation. The Journal of Specialised Translation, 25, 149–173.

Floranti, A. D. (2020). Indonesia–English Translation of Idiomatic Expressions in The Novel This Earth of Mankind. Buletin Alt-Turas, 26(2), 14.

Frankenberg, G. A. (2015). Training translators to use corpora hands-on: Challenges and reactions by a group of 13 students at a UK university. Journal Corpora, 210(3), 351–380.

Gharedaghi, M., Eslamieh, R., & Shahidi, H. R. (2019). Content Equivalence Analysis of Health News Translation: A Bakerian Approach. Theory and Practice in Language Studies, 9(9), 1198. https://doi.org/10.17507/tpls.0909.17

Green, S. (2013). The efficacy of human post-editing for language translation.

Hadley, J., Popovic, M., Afli, H., & Way, A. (2019). Proceedings of the 1st Workshop on Qualities of Literary Machine Translation.

Hakemi, B. G. (n.d.). The possibilities and limitations of literary translation: A review of J. Payne’s and Henri Clarke’s Translations of Ghazalyat of Hafez. 13.

Hermans, T. (2007). The conference of the tongues. St Jerome.

Hockey, S. (2001). Concordance programs for corpus linguistics” in corpus linguistics in north america: Selections from the 1999 symposium. 76–97.

Koponen, M. (2016). Is machine translation post-editing worth the effort? A survey of research into post-editing and effort. The Journal of Specialised Translation, 25, 131–148.

L, A. (2005). AntConc: Design and development of a freeware corpus analysis toolkit for the technical writing classroom. Professional Communication Conference, 729–737.

Lotz, S., & Van Rensburg, A. (2016). Omission And Other Sins: Tracking The Quality Of Online Machine Translation Output Over Four Years. Stellenbosch Papers In Linguistics, 46(0). https://doi.org/10.5774/46-0-223

Matusov, E. (2019). The Challenges of Using Neural Machine Translation for Literature. Proceedings of the Qualities of Literary Machine Translation, 10–19. https://www.aclweb.org/anthology/W19-7302

McEnery, T., & Wilson, A. (2001). Corpus linguistics. An introduction. (Second edition). Edinburgh University Press.

Noguchi, J. (2004). A genre analysis and minicorpora approach to support professional writing by nonnative English speakers. 11, 101–110.

Robimson, D. (1997). Becoming a translator: An introduction to the theory and practice of translation. Routledge Group.

Sayogie, F., & Supardi, M. (2019). Equivalence and Untranslatability in English Translations of UUD Negara Republik Indonesia 1945. Proceedings of the 2nd Internasional Conference on Culture and Language in Southeast Asia (ICCLAS 2018). Proceedings of the 2nd Internasional Conference on Culture and Language in Southeast Asia (ICCLAS 2018), Tangerang Selatan, Indonesia. https://doi.org/10.2991/icclas-18.2019.2

Shraideh, K. W., & Mahadin, R. S. (2015). Difficulties and Strategies in Translating Collocations in BBC Political Texts. Arab World English Journal, 37.

Taivalkoski-Shilov, K. (2019). Ethical issues regarding machine(-assisted) translation of literary texts. Perspectives, 27(5), 689–703. https://doi.org/10.1080/0907676X.2018.1520907

Tyulenev, S. (2015). Toward theorizing translation as an occupation. 2(1), 15–29.

Xiyao, H. (2015). How to Achieve Equivalence, the Eternal Issue in Translation Studies: A Review of In Other Words: A Coursebook on Translation. 4.

Zheng, H. (2015). A Case Study of Machine Translation: Problems and Suggestions. International Journal of English Linguistics, 5(2), p92. https://doi.org/10.5539/ijel.v5n2p92

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DOI: 10.15408/bat.v27i1.16916


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