Question Generation aims to automatically generate questions based on a given input provided as context. A controllable question generation scheme focuses on generating questions with specific attributes, allowing better control. In this study, we propose a few-shot prompting strategy for controlling the generation of question-answer pairs from childrens narrative texts. We aim to control two attributes: the questions explicitness and underlying narrative elements. With empirical evaluation, we show the effectiveness of controlling the generation process by employing few-shot prompting side by side with a reference model. Our experiments highlight instances where the few-shot strategy surpasses the reference model, particularly in scenarios such as semantic closeness evaluation and the diversity and coherency of question-answer pairs. However, these improvements are not always statistically significant. The code is publicly available at github.com/bernardoleite/few-shot-prompting-qg-control.

The task of Question Generation (QG) has attracted the interest of the natural language processing community in recent years. QG aims to automatically generate well-formed questions from an input (e.g., text), which can be especially relevant for computer-supported educational platforms. Recent work relies on large-scale question-answering (QA) datasets (in English) to train and build the QG systems. However, large-scale quality QA datasets are not widely available for lower-resourced languages. In this respect, this research addresses the task of QG in a lower-resourced language Portuguese using a traditional rule-based approach for generating wh-questions. We perform a feasibility analysis of the approach through a comprehensive evaluation supported by two studies: (1) comparing the similarity between machine-generated and human-authored questions using automatic metrics, and (2) comparing the perceived quality of machine-generated questions to those elaborated by humans. Although the results show that rule-based generated questions fall short in quality compared to those authored by humans, they also suggest that a rule-based approach remains a feasible alternative to neural-based techniques when these are not viable. The code is publicly available at https://github.com/bernardoleite/question-generation-portuguese.

Artificial Intelligence (AI) has seen numerous applications in the area of Education. Through the use of educational technologies such as Intelligent Tutoring Systems (ITS), learning possibilities have increased significantly. One of the main challenges for the widespread use of ITS is the ability to automatically generate questions. Bearing in mind that the act of questioning has been shown to improve the students learning outcomes, Automatic Question Generation (AQG) has proven to be one of the most important applications for optimizing this process. We present a tool for generating factual questions in Portuguese by proposing three distinct approaches. The first one performs a syntax-based analysis of a given text by using the information obtained from Part-of-speech tagging (PoS) and Named Entity Recognition (NER). The second approach carries out a semantic analysis of the sentences, through Semantic Role Labeling (SRL). The last method extracts the inherent dependencies within sentences using Dependency Parsing. All of these methods are possible thanks to Natural Language Processing (NLP) techniques. For evaluation, we have elaborated a pilot test that was answered by Portuguese teachers. The results verify the potential of these different approaches, opening up the possibility to use them in a teaching environment.