  As virtual assistants continue to be taken up globally, there is an
ever-greater need for these speech-based systems to communicate naturally in a
variety of languages. Crowdsourcing initiatives have focused on multilingual
translation of big, open data sets for use in natural language processing
(NLP). Yet, language translation is often not one-to-one, and biases can
trickle in. In this late-breaking work, we focus on the case of pronouns
translated between English and Japanese in the crowdsourced Tatoeba database.
We found that masculine pronoun biases were present overall, even though
plurality in language was accounted for in other ways. Importantly, we detected
biases in the translation process that reflect nuanced reactions to the
presence of feminine, neutral, and/or non-binary pronouns. We raise the issue
of translation bias for pronouns and offer a practical solution to embed
plurality in NLP data sets.
