  Written text often provides sufficient clues to identify the author, their
gender, age, and other important attributes. Consequently, the authorship of
training and evaluation corpora can have unforeseen impacts, including
differing model performance for different user groups, as well as privacy
implications. In this paper, we propose an approach to explicitly obscure
important author characteristics at training time, such that representations
learned are invariant to these attributes. Evaluating on two tasks, we show
that this leads to increased privacy in the learned representations, as well as
more robust models to varying evaluation conditions, including out-of-domain
corpora.
