Despite the importance of understanding causality, corpora addressing causal relations are
limited. There is a discrepancy between existing annotation guidelines of event causality …

Asking good questions is an essential ability for both human and machine intelligence.
However, existing neural question generation approaches mainly focus on the short factoid …

Automatic text generation is the generation of natural language text by machines. Enabling
machines to generate readable and coherent text is one of the most vital yet challenging …

Questioning plays a vital role in education, directing knowledge construction and assessing
students' understanding. However, creating high-level questions requires significant …

Automated question generation has made great advances with the help of large NLP
generation models. However, typically only one question is generated for each intended …

Developing an educational test can be expensive and time-consuming, as each item must
be written by experts and then evaluated by collecting hundreds of student responses …

Background Asking learners manually authored questions about their readings improves
their text comprehension. Yet, not all reading materials comprise sufficiently many questions …

We propose a type-controlled framework for inquisitive question generation. We annotate an
inquisitive question dataset with question types, train question type classifiers, and finetune …

Current causal text mining datasets vary in objectives, data coverage, and annotation
schemes. These inconsistent efforts prevent modeling capabilities and fair comparisons of …

Question Answer Generation (QAG) is an effective data augmentation technique to improve
the accuracy of question answering systems, especially in low-resource domains. While …