  Recently, open-domain question answering systems have begun to rely heavily
on annotated datasets to train neural passage retrievers. However, manually
annotating such datasets is both difficult and time-consuming, which limits
their availability for less popular languages. In this work, we experiment with
several methods for automatically collecting weakly labeled datasets and show
how they affect the performance of the neural passage retrieval models. As a
result of our work, we publish the MAUPQA dataset, consisting of nearly 400,000
question-passage pairs for Polish, as well as the HerBERT-QA neural retriever.
