  We propose a multilingual data-driven method for generating reading
comprehension questions using dependency trees. Our method provides a strong,
mostly deterministic, and inexpensive-to-train baseline for less-resourced
languages. While a language-specific corpus is still required, its size is
nowhere near those required by modern neural question generation (QG)
architectures. Our method surpasses QG baselines previously reported in the
literature and shows a good performance in terms of human evaluation.
