  Supervised machine learning models and their evaluation strongly depends on
the quality of the underlying dataset. When we search for a relevant piece of
information it may appear anywhere in a given passage. However, we observe a
bias in the position of the correct answer in the text in two popular Question
Answering datasets used for passage re-ranking. The excessive favoring of
earlier positions inside passages is an unwanted artefact. This leads to three
common Transformer-based re-ranking models to ignore relevant parts in unseen
passages. More concerningly, as the evaluation set is taken from the same
biased distribution, the models overfitting to that bias overestimate their
true effectiveness. In this work we analyze position bias on datasets, the
contextualized representations, and their effect on retrieval results. We
propose a debiasing method for retrieval datasets. Our results show that a
model trained on a position-biased dataset exhibits a significant decrease in
re-ranking effectiveness when evaluated on a debiased dataset. We demonstrate
that by mitigating the position bias, Transformer-based re-ranking models are
equally effective on a biased and debiased dataset, as well as more effective
in a transfer-learning setting between two differently biased datasets.
