Towards Enriched Controllability for Educational Question Generation

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2023

Autores

Leite, B (Autor) Outra; Henrique Lopes Cardoso (Autor) FEUP

Ata de Conferência Internacional

Título: ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2023

Páginas: 786-791

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

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

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6