Neural Question Generation for the Portuguese Language: A Preliminary Study

Abstract

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. The code and models are publicly available at https://github.com/bernardoleite/question-generation-t5-pytorch-lightning.