Neural Question Generation for the Portuguese Language: A Preliminary Study

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2022

Autores

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

Ata de Conferência Internacional

Título: Progress in Artificial Intelligence - 21st EPIA Conference on Artificial Intelligence, EPIA 2022, Lisbon, Portugal, August 31 - September 2, 2022, Proceedings

Páginas: 780-793

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 1 Citação

Outras Informações

ID Authenticus: P-00X-4T3

DOI: 10.1007/978-3-031-16474-3_63

Abstract (EN): Question Generation (QG) is an important and challenging problem that has attracted attention from the natural language processing (NLP) community over the last years. QG aims to automatically generate questions given an input. Recent studies in this field typically use widely available question-answering (QA) datasets (in English) and neural models to train and build these QG systems. As lower-resourced languages (e.g. Portuguese) lack large-scale quality QA data, it becomes a significant challenge to experiment with recent neural techniques. This study uses a Portuguese machine-translated version of the SQuAD v1.1 dataset to perform a preliminary analysis of a neural approach to the QG task for Portuguese. We frame our approach as a sequence-to-sequence problem by fine-tuning a pre-trained language model - T5 for generating factoid (or wh)-questions. Despite the evident issues that a machine-translated dataset may bring when using it for training neural models, the automatic evaluation of our Portuguese neural QG models presents results in line with those obtained for English. To the best of our knowledge, this is the first study addressing Neural QG for Portuguese.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 14