Source language classification of indirect translations
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.
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
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