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Investigating translated Chinese and its variants using machine learning

Published online by Cambridge University Press:  03 April 2020

Hai Hu Open the ORCID record for Hai Hu [Opens in a new window]  and
Sandra Kübler Open the ORCID record for Sandra Kübler [Opens in a new window]
Hai Hu*
Affiliation:
Department of Linguistics, Indiana University, Bloomington, IN, USA
Sandra Kübler
Affiliation:
Department of Linguistics, Indiana University, Bloomington, IN, USA
*
*Corresponding author. E-mail: [email protected]
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Abstract

Translations are generally assumed to share universal features that distinguish them from texts that are originally written in the same language. Thus, we can argue that these translations constitute their own variety of a language, often called translationese. However, translations are also influenced by their source languages and thus show different characteristics depending on the source language. Consequently, we argue that these variants constitute different “dialects” of translations into the same target language. Studies using machine learning techniques on Indo-European languages have investigated the universal characteristics of translationese and how translations from various source languages differ. However, for typologically very different languages such as Chinese, there are only few corpus studies that tap into the intricate relation between translations and the originals, as well as into the relations among translations themselves. In this contribution, we investigate the following questions: (1) What are the characteristics of Chinese translationese, both in general and with respect to different source languages? (2) Can we find differences not only at the lexical but also on the syntactic level? and (3) Based on the characteristics found in the previous questions, which of the proposed laws and universals can we corroborate based on our evidence from Chinese? We use machine learning to operationalize determining the importance of different characteristics and comparing their importance for our Chinese dataset with characteristics previously reported in studies on English. In addition, our methodology allows us to add syntactic features, which have rarely been used to study translations into Chinese. Our results show that Chinese translations as a whole can be reliably distinguished from non-translations, even based on only five features. More interestingly, typological traces from the source languages can often be found in their translations, therefore creating what we call dialects of translationese. For instance, translations from two Altaic languages exhibit more noun repetition and less frequent use of pronouns. Additionally, some characteristics that are not discriminative for English work well for Chinese, possibly because the distance between Chinese and the source languages is greater than that in English studies.

Keywords

TranslationeseText classificationChineseTranslation universal
Type
Article
Information
Natural Language Engineering , Volume 27 , Issue 3 , May 2021 , pp. 339 - 372
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Copyright
© Cambridge University Press 2020

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