Neural Question Generation for the Portuguese Language: A Preliminary Study

• Type: Article in International Conference Proceedings Book
• Date: 2022

Authors

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

Conference proceedings International

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

Pages: 780-793

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 1 Citation

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14