Towards Enriched Controllability for Educational Question Generation
Bernardo Leite Henrique Lopes Cardoso
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
github.com/bernardoleite/question-generation-control.