Towards Enriched Controllability for Educational Question Generation

• Type: Article in International Conference Proceedings Book
• Date: 2023

Authors

Leite, B (Author) Other; Henrique Lopes Cardoso (Author) FEUP

Conference proceedings International

Title: ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2023

Pages: 786-791

24th International Conference on Artificial Intelligence in Education (AIED)

Int Artificial Intelligence Educ Soc, Tokyo, JAPAN, JUL 03-07, 2023

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-00Y-K8Z

DOI: 10.1007/978-3-031-36272-9_72

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6