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 github.com/bernardoleite/question-generation-control.

翻译：问题生成（QG）是自然语言处理（NLP）中的一项任务，旨在根据输入（通常包含文本和目标答案）自动生成问题。近期关于QG的研究致力于控制生成的问题类型，以满足教育需求。在教育QG中，可控性的一个显著示例是生成涉及特定叙事元素（如因果关系、结局解决或预测）的问题。本研究通过引入新的引导属性——问题显性度——来丰富QG的可控性。我们提出从儿童友好故事中控制显性和隐性wh-问题的生成。初步实验证据表明，可单独通过问题显性度实现QG控制，也可同时结合另一目标属性（问题的叙事元素）进行控制。相关代码已在github.com/bernardoleite/question-generation-control公开。