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What can research on indirect translation do for Translation Studies?
Edited by Hanna Pięta, Laura Ivaska and Yves Gambier
[Target 34:3] 2022
► pp. 370–394

Source language classification of indirect translations

ORCID logo | University of Turku
ORCID logo | University of Turku | Finnish Literature Society (SKS)
One of the major barriers to the systematic study of indirect translation – that is, translations of translations – is the lack of efficient methods to identify these translations. In this article, we use supervised machine learning to examine whether computers can be harnessed to identify indirect translations. Our data consist of a monolingual comparable corpus that includes (1) nontranslated Finnish texts, (2) direct translations from English, French, German, Greek, and Swedish into Finnish, and (3) indirect translations from Greek (the ultimate source language) via English, French, German, and Swedish (mediating languages) into Finnish. We use n-grams of various types and lengths as feature sets and random forests as the statistical classification technique. To maximize the transferability of the method, the feature sets were implemented in accordance with the Universal Dependencies framework. This study confirms that computers can distinguish between translated and nontranslated Finnish, as well as between Finnish translations made from different source languages. Regarding indirect translations, the ultimate source language has a greater impact on the linguistic composition of indirect Finnish translations than their respective mediating languages. Hence, the indirect translations could not be reliably identified. Therefore, our results suggest that the reliable computational identification of indirect translations and their mediating languages requires a way to control for the effect of the ultimate source language.
Keywords: indirect translation, literary translation, supervised machine learning, source language identification, corpus-based translation studies, Finnish
Article outline
  • 1.Introduction
  • 2.Related work
    • 2.1Distinguishing translations and nontranslations
    • 2.2Classifying direct translations according to their source language
    • 2.3Classifying indirect translations according to their linguistic features
  • 3.Data and methods
    • 3.1Data description and preprocessing
    • 3.2Feature sets
      • 3.2.1Sequential n-grams
      • 3.2.2Dependency 2-grams
      • 3.2.3Positional 1-grams
      • 3.2.4Character 3-grams
    • 3.3Experimental setup and statistical evaluation
  • 4.Results
    • 4.1Distinguishing translated and nontranslated Finnish
    • 4.2Classifying Finnish translations based on their source languages
    • 4.3Classifying indirect Greek–Finnish translations according to their mediating languages
  • 5.Conclusions
  • Notes
  • References
Published online: 11 April 2022
DOI logo
https://doi.org/10.1075/target.00006.iva
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