  Reading comprehension is a well studied task, with huge training datasets in
English. This work focuses on building reading comprehension systems for Czech,
without requiring any manually annotated Czech training data. First of all, we
automatically translated SQuAD 1.1 and SQuAD 2.0 datasets to Czech to create
training and development data, which we release at
http://hdl.handle.net/11234/1-3249. We then trained and evaluated several BERT
and XLM-RoBERTa baseline models. However, our main focus lies in cross-lingual
transfer models. We report that a XLM-RoBERTa model trained on English data and
evaluated on Czech achieves very competitive performance, only approximately 2
percent points worse than a~model trained on the translated Czech data. This
result is extremely good, considering the fact that the model has not seen any
Czech data during training. The cross-lingual transfer approach is very
flexible and provides a reading comprehension in any language, for which we
have enough monolingual raw texts.
