Quinductor: a multilingual data-driven method for generating reading-comprehension questions using Universal Dependencies
This provides an inexpensive and effective baseline for question generation in low-resource languages, though it is incremental as it builds on existing dependency-based approaches.
The authors tackled the problem of generating reading comprehension questions for less-resourced languages by proposing a multilingual data-driven method using dependency trees, which surpassed previous baselines and performed well in human evaluations.
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.