  Coupled with the availability of large scale datasets, deep learning
architectures have enabled rapid progress on the Question Answering task.
However, most of those datasets are in English, and the performances of
state-of-the-art multilingual models are significantly lower when evaluated on
non-English data. Due to high data collection costs, it is not realistic to
obtain annotated data for each language one desires to support.
  We propose a method to improve the Cross-lingual Question Answering
performance without requiring additional annotated data, leveraging Question
Generation models to produce synthetic samples in a cross-lingual fashion. We
show that the proposed method allows to significantly outperform the baselines
trained on English data only. We report a new state-of-the-art on four
multilingual datasets: MLQA, XQuAD, SQuAD-it and PIAF (fr).
