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

arXiv 2211.15568 ↗First posted 28 Nov 2022cs.CLcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 20 references that are in the map. First posted on arXiv 28 Nov 2022.

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References · 8

  1. 2020Stanza: A Python Natural Language Processing Toolkit for Many Human Languagescited by 719
  2. 2019Unified Language Model Pre-training for Natural Language Understanding and Generationcited by 668
  3. 2020Universal Dependencies v2: An Evergrowing Multilingual Treebank Collectioncited by 269
  4. 2017Relevance of Unsupervised Metrics in Task-Oriented Dialogue for Evaluating Natural Language Generationcited by 61
  5. 2020Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word Ordercited by 28
  6. 2021BERT-based distractor generation for Swedish reading comprehension questions using a small-scale datasetcited by 12
  7. 2021Quinductor: a multilingual data-driven method for generating reading-comprehension questions using Universal Dependenciescited by 3
  8. 2021Minor changes make a difference: a case study on the consistency of UD-based dependency parserscited by 2

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