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Quinductor: a multilingual data-driven method for generating reading-comprehension questions using Universal Dependencies

2021-03-18Code Available0· sign in to hype

Dmytro Kalpakchi, Johan Boye

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Abstract

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.

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