  Cross-language authorship attribution problems rely on either translation to
enable the use of single-language features, or language-independent feature
extraction methods. Until recently, the lack of datasets for this problem
hindered the development of the latter, and single-language solutions were
performed on machine-translated corpora. In this paper, we present a novel
language-independent feature for authorship analysis based on dependency graphs
and universal part of speech tags, called DT-grams (dependency tree grams),
which are constructed by selecting specific sub-parts of the dependency graph
of sentences. We evaluate DT-grams by performing cross-language authorship
attribution on untranslated datasets of bilingual authors, showing that, on
average, they achieve a macro-averaged F1 score of 0.081 higher than previous
methods across five different language pairs. Additionally, by providing
results for a diverse set of features for comparison, we provide a baseline on
the previously undocumented task of untranslated cross-language authorship
attribution.
