  Question answering is one of the most challenging tasks in language
understanding. Most approaches are developed for English, while less-resourced
languages are much less researched. We adapt a successful English
question-answering approach, called UnifiedQA, to the less-resourced Slovene
language. Our adaptation uses the encoder-decoder transformer SloT5 and mT5
models to handle four question-answering formats: yes/no, multiple-choice,
abstractive, and extractive. We use existing Slovene adaptations of four
datasets, and machine translate the MCTest dataset. We show that a general
model can answer questions in different formats at least as well as specialized
models. The results are further improved using cross-lingual transfer from
English. While we produce state-of-the-art results for Slovene, the performance
still lags behind English.
