  Several proposals have been put forward in recent years for improving
out-of-distribution (OOD) performance through mitigating dataset biases. A
popular workaround is to train a robust model by re-weighting training examples
based on a secondary biased model. Here, the underlying assumption is that the
biased model resorts to shortcut features. Hence, those training examples that
are correctly predicted by the biased model are flagged as being biased and are
down-weighted during the training of the main model. However, assessing the
importance of an instance merely based on the predictions of the biased model
may be too naive. It is possible that the prediction of the main model can be
derived from another decision-making process that is distinct from the behavior
of the biased model. To circumvent this, we introduce a fine-tuning strategy
that incorporates the similarity between the main and biased model attribution
scores in a Product of Experts (PoE) loss function to further improve OOD
performance. With experiments conducted on natural language inference and fact
verification benchmarks, we show that our method improves OOD results while
maintaining in-distribution (ID) performance.
