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

TL;DR

Quinductor is a multilingual, data-driven approach for generating reading comprehension questions using dependency trees, offering a resource-efficient alternative to neural models, with strong performance and good human evaluation results.

Contribution

It introduces a mostly deterministic, inexpensive baseline method for multilingual question generation that requires less data than neural approaches.

Findings

Outperforms previous QG baselines in literature

Requires significantly less training data

Achieves high human evaluation scores

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.