  Learning representations which remain invariant to a nuisance factor has a
great interest in Domain Adaptation, Transfer Learning, and Fair Machine
Learning. Finding such representations becomes highly challenging in NLP tasks
since the nuisance factor is entangled in a raw text. To our knowledge, a major
issue is also that only few NLP datasets allow assessing the impact of such
factor. In this paper, we introduce two generalization metrics to assess model
robustness to a nuisance factor: \textit{generalization under target bias} and
\textit{generalization onto unknown}. We combine those metrics with a simple
data filtering approach to control the impact of the nuisance factor on the
data and thus to build experimental biased datasets. We apply our method to
standard datasets of the literature (\textit{Amazon} and \textit{Yelp}). Our
work shows that a simple text classification baseline (i.e., sentiment analysis
on reviews) may be badly affected by the \textit{product ID} (considered as a
nuisance factor) when learning the polarity of a review. The method proposed is
generic and applicable as soon as the nuisance variable is annotated in the
dataset.
