That's definitely one of our main areas for future research. So far, the only part of the paper where we consider other languages is in studying how model bias affects language translation:

Unsurprisingly, today’s statistical machine translation systems reflect existing gender stereotypes. Translations to English from many gender-neutral languages such as Finnish, Estonian, Hungarian, Persian, and Turkish lead to gender-stereotyped sentences. For example, Google Translate converts these Turkish sentences with genderless pronouns: "O bir doktor. O bir hems¸ire." to these English sentences: "He is a doctor. She is a nurse." A test of the 50 occupation words used in the results presented in Figure 1 shows that the pronoun is translated to “he” in the majority of cases and "she" in about a quarter of cases; tellingly, we found that the gender association of the word vectors almost perfectly predicts which pronoun will appear in the translation.

See section on "Effects of bias in NLP applications" http://randomwalker.info/publications/language-bias.pdf