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.
@article{arxiv.2103.10121,
title = {Quinductor: a multilingual data-driven method for generating reading-comprehension questions using Universal Dependencies},
author = {Dmytro Kalpakchi and Johan Boye},
journal= {arXiv preprint arXiv:2103.10121},
year = {2023}
}
Comments
Added the DOI link to the peer reviewed version accepted to the Natural Language Engineering Journal