Are Translated Texts Useful for Gradient Word Order Extraction?

Gradient, token-level measures of word order preferences within a language are useful both for cross-linguistic comparison in linguistic typology and for multilingual NLP applications.However, such measures might not be representative of general language use when extracted from translated corpora, due to noise introduced by structural effects of translation.We attempt to quantify this uncertainty in a case study of subject/verb order statistics extracted from a parallel corpus of parliamentary speeches in 21 European languages.We find that word order proportions in translated texts generally resemble those extracted from nontranslated texts, but tend to skew somewhat toward the dominant word order of the target language.We also investigate the potential presence of underlying source language-specific effects, but find that they do not sufficiently explain the variation across translations.