# Automatically generating question-answer pairs for assessing basic reading comprehension in Swedish

arXiv: https://arxiv.org/abs/2211.15568 · HTML: https://mlsanity.com/paper/2211.15568/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 28 Nov 2022.

## Builds on (most cited first, 8 of 8)

- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://mlsanity.com/paper/1905.03197/) (2019)
- [Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection](https://mlsanity.com/paper/2004.10643/) (2020)
- [Relevance of Unsupervised Metrics in Task-Oriented Dialogue for Evaluating Natural Language Generation](https://mlsanity.com/paper/1706.09799/) (2017)
- [Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word Order](https://mlsanity.com/paper/2004.11579/) (2020)
- [BERT-based distractor generation for Swedish reading comprehension questions using a small-scale dataset](https://mlsanity.com/paper/2108.03973/) (2021)
- [Quinductor: a multilingual data-driven method for generating reading-comprehension questions using Universal Dependencies](https://mlsanity.com/paper/2103.10121/) (2021)
- [Minor changes make a difference: a case study on the consistency of UD-based dependency parsers](https://mlsanity.com/paper/2111.15413/) (2021)

## Abstract (arXiv)

This paper presents an evaluation of the quality of automatically generated reading comprehension questions from Swedish text, using the Quinductor method. This method is a light-weight, data-driven but non-neural method for automatic question generation (QG). The evaluation shows that Quinductor is a viable QG method that can provide a strong baseline for neural-network-based QG methods.