  Despite impressive performance on many text classification tasks, deep neural
networks tend to learn frequent superficial patterns that are specific to the
training data and do not always generalize well. In this work, we observe this
limitation with respect to the task of native language identification. We find
that standard text classifiers which perform well on the test set end up
learning topical features which are confounds of the prediction task (e.g., if
the input text mentions Sweden, the classifier predicts that the author's
native language is Swedish). We propose a method that represents the latent
topical confounds and a model which "unlearns" confounding features by
predicting both the label of the input text and the confound; but we train the
two predictors adversarially in an alternating fashion to learn a text
representation that predicts the correct label but is less prone to using
information about the confound. We show that this model generalizes better and
learns features that are indicative of the writing style rather than the
content.
