  Recent work has shown evidence of 'Clever Hans' behavior in high-performance
neural translationese classifiers, where BERT-based classifiers capitalize on
spurious correlations, in particular topic information, between data and target
classification labels, rather than genuine translationese signals.
Translationese signals are subtle (especially for professional translation) and
compete with many other signals in the data such as genre, style, author, and,
in particular, topic. This raises the general question of how much of the
performance of a classifier is really due to spurious correlations in the data
versus the signals actually targeted for by the classifier, especially for
subtle target signals and in challenging (low resource) data settings. We focus
on topic-based spurious correlation and approach the question from two
directions: (i) where we have no knowledge about spurious topic information and
its distribution in the data, (ii) where we have some indication about the
nature of spurious topic correlations. For (i) we develop a measure from first
principles capturing alignment of unsupervised topics with target
classification labels as an indication of spurious topic information in the
data. We show that our measure is the same as purity in clustering and propose
a 'topic floor' (as in a 'noise floor') for classification. For (ii) we
investigate masking of known spurious topic carriers in classification. Both
(i) and (ii) contribute to quantifying and (ii) to mitigating spurious
correlations.
