Abstract
Question Generation (QG) is a task within Natural Language Processing (NLP) that involves automatically generating questions given an input, typically composed of a text and a target answer. Recent work on QG aims to control the type of generated questions so that they meet educational needs. A remarkable example of controllability in educational QG is the generation of questions underlying certain narrative elements, e.g., causal relationship, outcome resolution, or prediction. This study aims to enrich controllability in QG by introducing a new guidance attribute: question explicitness. We propose to control the generation of explicit and implicit (wh)-questions from children-friendly stories. We show preliminary evidence of controlling QG via question explicitness alone and simultaneously with another target attribute: the question’s narrative element. The code is publicly available at https://github.com/bernardoleite/question-generation-control.
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Notes
- 1.
Summarization skills have been used to assess and improve students’ reading comprehension ability [8].
- 2.
Detailed information of each aspect is described in the FairytaleQA paper [9].
- 3.
A colon separates the input and output information used by the models.
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- 5.
Note that the drop in QG ROUGE\(_L\)-F1 values relative to baseline model B is expected, since in these models the answer is not included in the input. The generated questions may thus focus on target answers that are not part of the gold standard.
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Acknowledgments
This work was financially supported by Base Funding - UIDB/00027/2020 of the Artificial Intelligence and Computer Science Laboratory - LIACC - funded by national funds through the FCT/MCTES (PIDDAC). Bernardo Leite is supported by a PhD studentship (with reference 2021.05432.BD), funded by Fundação para a Ciência e a Tecnologia (FCT).
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Leite, B., Cardoso, H.L. (2023). Towards Enriched Controllability for Educational Question Generation. In: Wang, N., Rebolledo-Mendez, G., Matsuda, N., Santos, O.C., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2023. Lecture Notes in Computer Science(), vol 13916. Springer, Cham. https://doi.org/10.1007/978-3-031-36272-9_72
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