  Diagnostic datasets that can detect biased models are an important
prerequisite for bias reduction within natural language processing. However,
undesired patterns in the collected data can make such tests incorrect. For
example, if the feminine subset of a gender-bias-measuring coreference
resolution dataset contains sentences with a longer average distance between
the pronoun and the correct candidate, an RNN-based model may perform worse on
this subset due to long-term dependencies. In this work, we introduce a
theoretically grounded method for weighting test samples to cope with such
patterns in the test data. We demonstrate the method on the GAP dataset for
coreference resolution. We annotate GAP with spans of all personal names and
show that examples in the female subset contain more personal names and a
longer distance between pronouns and their referents, potentially affecting the
bias score in an undesired way. Using our weighting method, we find the set of
weights on the test instances that should be used for coping with these
correlations, and we re-evaluate 16 recently released coreference models.
