MAUPQA: Massive Automatically-created Polish Question Answering Dataset
This work addresses the problem of limited training data for open-domain question answering systems in Polish, providing a resource for researchers and developers in natural language processing for low-resource languages.
The authors tackled the scarcity of annotated datasets for training neural passage retrievers in less popular languages by experimenting with automatic collection methods, resulting in the MAUPQA dataset with nearly 400,000 Polish question-passage pairs and the HerBERT-QA retriever.
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.