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Article published In:
International Journal of Corpus Linguistics
Vol. 27:1 (2022) ► pp.31–58

Universals in machine translation?

A corpus-based study of Chinese-English translations by WeChat Translate

ORCID logo | The Hong Kong Polytechnic University
| The Hong Kong Polytechnic University
By examining and comparing the linguistic patterns in a self-built corpus of Chinese-English translations produced by WeChat Translate, the latest online machine translation app from the most popular social media platform (WeChat) in China, this study explores such questions as whether or not and to what extent simplification and normalization (hypothesized Translation Universals) exhibit themselves in these translations. The results show that, whereas simplification cannot be substantiated, the tendency of normalization to occur in the WeChat translations can be confirmed. The research finds that these results are caused by the operating mechanism of machine translation (MT) systems. Certain salient words tend to prime WeChat’s MT system to repetitively resort to typical language patterns, which leads to a significant overuse of lexical chunks. It is hoped that the present study can shed new light on the development of MT systems and encourage more corpus-based product-oriented research on MT.
Keywords: Translation Universals, machine translation, WeChat Translate , simplification, normalization
Article outline
  • 1.Introduction
  • 2.Translation Universals
  • 3.Corpora and methodology
    • 3.1Corpora compilation
    • 3.2Methodology
  • 4.Findings and discussion
    • 4.1Results for simplification
    • 4.2Results for normalization
      • 4.2.1NV Ratio
      • 4.2.2Correction of punctuation
      • 4.2.3The overuse of typical grammatical patterns
    • 4.3Discussion
  • 5.Conclusion
  • Acknowledgements
  • References
Published online: 14 February 2022
DOI logo
https://doi.org/10.1075/ijcl.19127.luo
References (52)
References
Baker, M. (1993). Corpus linguistics and translation studies: Implications and applications. In M. Baker, G. Francis, & E. Tognini-Bonelli (Eds.), Text and Technology: In Honour of John Sinclair (pp. 233–250). John Benjamins. DOI logoGoogle Scholar
(1996). Corpus-based translation studies: The challenges that lie ahead. In H. Somers (Ed.), In Terminology, LSP and Translation: Studies in Language Engineering, in Honour of Juan C. Sager (pp. 175–186). John Benjamins. DOI logoGoogle Scholar
Bernardini, S., & Ferraresi, A. (2011). Practice, description and theory come together: Normalization or interference in Italian technical translation? Meta, 56 (2), 226–246. DOI logoGoogle Scholar
Bernardini, S., Ferraresi, A., & Miličević, M. (2016). From EPIC to EPTIC: Exploring simplification in interpreting and translation from an intermodal perspective. Target, 28 (1), 61–86. DOI logoGoogle Scholar
Blum-Kulka, S., & Levenston, E. (1983). Universals of lexical simplification. In C. Faerch & C. Gabriele (Eds.), Strategies in Inter-language Communication (pp. 119–139). Longman.Google Scholar
Bo, B. (2002). English Grammar. Kai Ming Press.Google Scholar
Cappelle, B., & Loock, R. (2017). Typological differences shining through: The case of phrasal verbs in translated English. In G. De Sutter, M. A. Lefer, & I. Delaere (Eds.), Empirical Translation Studies: New Theoretical and Methodological Traditions (pp. 235–264). Mouton de Gruyter. DOI logoGoogle Scholar
Chesterman, A. (2004). Beyond the particular. In A. Mauranen & P. Kujamäki (Eds), Translation Universals: Do They Exist? (pp. 33–50). John Benjamins. DOI logoGoogle Scholar
Cronin, M. (2013). Translation in the Digital Age. Routledge.Google Scholar
Feng, Z. (2018). Parallel development of machine translation and artificial intelligence. Journal of Foreign Language, 41 (6), 35–48.Google Scholar
(2020). Rosetta Stone and machine translation. Foreign Language Research, 1 1, 1–17.Google Scholar
(2021). Two-wheel driven natural language understanding. Frontiers in Corpus Studies, 1 1, 148–175.Google Scholar
Grabowski, L. (2013). Interfacing corpus linguistics and computational stylistics: Translation universals in translational literary Polish. International Journal of Corpus Linguistics, 18 (2), 254–280. DOI logoGoogle Scholar
Gupta, A. (2021). User-controlled content translation in social media. In 26th International Conference on Intelligent User Interfaces – Companion (pp. 96–98). Association for Computing Machinery. DOI logoGoogle Scholar
Halverson, S. (2003). The cognitive basis of Translation Universals. Target, 15 (2), 197–241. DOI logoGoogle Scholar
Hoey, M. (2005). Lexical Priming: A New Theory of Words and Language. Routledge.Google Scholar
Holmes, J. (1988). Translated!: Papers on Literary Translation and Translation Studies. Rodopi. DOI logoGoogle Scholar
Hu, K. (2016). Introducing Corpus-Based Translation Studies. Springer; Shanghai Jiao Tong University Press. DOI logoGoogle Scholar
Hu, K., & Li, Y. (2016). Features of machine translation and its relations with human translation. Chinese Translators Journal, 5 1, 6–14.Google Scholar
Hu, X., & Zeng, J. (2011). 从“把”字句看翻译汉语的杂合特征 [Hybridization of translated Chinese as observed in the use of “ba” constructions]. Foreign Language Research, 130 1, 69–75.Google Scholar
Joos, M. (1967). The Five Clocks. Harcourt, Brace & World.Google Scholar
Kajzer-Wietrzny, M. (2015). Simplification in interpreting and translation. Across Languages and Cultures, 16 (2), 233–255. DOI logoGoogle Scholar
Kenny, D. (2001). Lexis And Creativity In Translation: A Corpus-Based Study. St. Jerome.Google Scholar
Kruger, H., & Rooy, B. (2012). Register and the features of translated language. Across Languages and Cultures, 13 (1), 33–65. DOI logoGoogle Scholar
Lapshinova-Koltunski, E. (2015). Variation in translation: Evidence from corpora. In C. Fantinuoli & F. Zanetti (Eds.), New Directions in Corpus-Based Translation Studies (pp. 93–114). Language Science Press.Google Scholar
Laviosa, S. (1998). Core patterns of lexical use in a comparable corpus of English narrative prose. Meta, 43(4), 557–570. DOI logoGoogle Scholar
(2002). Corpus-based Translation Studies: Theory, Findings, Applications. Rodopi. DOI logoGoogle Scholar
(2006). Data-driven learning for translating anglicisms in business communication. IEEE Transactions on Professional Communication, 49(3), 267–274. DOI logoGoogle Scholar
(2010). Corpus-based translation studies 15 years on: Theory, findings, applications. Synaps, 24 1(2010), 3–12.Google Scholar
(2011). Corpus-based translation studies: Where does it come from? Where is it going? In A. Kruger, K. Wallmach, & J. Munday (Eds.), Corpus-based Translation Studies: Research and Applications (pp. 13–32). Continuum.Google Scholar
Lian, S. (2010). 英汉对比研究 [Contrastive Studies of English and Chinese]. Higher Education Press.Google Scholar
Liu, F. (2019, Oct. 24). 微信牵手网易有道 [WeChat and Youdao in Cooperation]. ZOL Soft. [URL]
Mao, L. (2014). 微信与微语言生活 [WeChat and WeChat language]. Social Science Front, 12 1, 136–141.Google Scholar
Page, R. (2012). Stories and Social Media: Identities and Interaction. Routledge.Google Scholar
Rayson, P. (2008). From key words to key semantic domains. International Journal of Corpus Linguistics, 13 (4), 519–549. DOI logoGoogle Scholar
Sánchez-Moya, A., & Cruz-Moya, O. (2015). “Hey there! I am using WhatsApp”: A preliminary study of recurrent discursive realisations in a corpus of WhatsApp statuses. Procedia: Social and Behavioral Sciences, 212 (C), 52–60. DOI logoGoogle Scholar
Schwartz, L. (2018). The history and promise of machine translation. In I. Lacruz & R. Jääskeläinen (Eds.), Innovation and Expansion in Translation Process Research (pp. 168–198). John Benjamins. DOI logoGoogle Scholar
Scott, M. (2012). WordSmith Tools (Version 6.0) [Computer software]. Lexical Analysis Software. [URL]
Stubbs, M. (1996). Text and Corpus Analysis: Computer-assisted Studies of Language and Culture. Blackwell.Google Scholar
Szymor, N. (2018). Translation: Universals or cognition? Target: International Journal of Translation Studies, 30 (1), 53–86. DOI logoGoogle Scholar
Tagliamonte, S., & Denis, D. (2008). Linguistic ruin? LOL! Instant messaging and teen language. American Speech, 83 (1), 3–34. DOI logoGoogle Scholar
Teich, E. (2003). Cross-Linguistic Variation in System and Text: A Methodology for the Investigation of Translations and Comparable Texts. Mouton de Gruyter. DOI logoGoogle Scholar
Toury, G. (2004). Probabilistic explanations in translation studies: Welcome as they are, would they qualify as universals? In A. Mauranen & P. Kujamäki (Eds), Translation Universals: Do they Exist? (pp. 15–32). John Benjamins. DOI logoGoogle Scholar
(2012). Descriptive Translation Studies and Beyond (2nd ed.). John Benjamins. DOI logoGoogle Scholar
Vanderauwera, R. (1985). Dutch Novels Translated into English. Rodopi. DOI logoGoogle Scholar
Van Oost, A., Willems, A., & De Sutter, G. (2016). Asymmetric syntactic patterns in German-Dutch translation: A corpus-based study of the interaction between normalisation and shining through. International Journal of Translation, 10 1, 1–18.Google Scholar
Vanmassenhove, E., Shterionov, D., & Way, A. (2019). Lost in translation: Loss and decay of linguistic richness in machine translation. In M. Forcada, A. Way, B. Haddow, & Sennrich (Eds.), Proceedings of Machine Translation Summit XVII: Research Track (pp. 222–232). European Association for Machine Translation. [URL]
Wang, X., & Li, X. (2016). A corpus-based study of normalization in Chinese translations of Shakespeare’s plays. Journal of Foreign Languages, 39 (3), 106–112.Google Scholar
Wang, B., Shan, D., Fan, A., Liu, L., & Guo, J. (2022). A sentiment classification method of web social media based on multidimensional and multilevel modelling. IEEE Transactions on Industrial Informatics, 18 (2), 1240–1249. DOI logoGoogle Scholar
Williams, D. (2005). Recurrent Features of Translation in Canada: A Corpus-based Study. University of Ottawa.Google Scholar
Zhang, M., & Toral, A. (2019). The effect of Translationese in machine translation test sets. In O. Bojar, R. Chatterjee, C. Federmann, M. Fishel, Y. Graham, B. Haddow, M. Huck, A. J. Yepes, P. Koehn, A. Martins, C. Monz, M. Negri, A. Névéol, M. Neves, M. Post, M. Turchi, & K. Verspoor (Eds.), Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers) (pp. 73–81). Association for Computational Linguistics. [URL]. DOI logo
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