This article offers a comprehensive review of the research on Natural Language Generation
(NLG) over the past two decades, especially in relation to data-to-text generation and text-to …

Question generation is an important yet challenging problem in Artificial Intelligence (AI),
which aims to generate natural and relevant questions from various input formats, eg …

We conduct a feasibility study into the applicability of answer-agnostic question generation
models to textbook passages. We show that a significant portion of errors in such systems …

Inquisitive probing questions come naturally to humans in a variety of settings, but is a
challenging task for automatic systems. One natural type of question to ask tries to fill a gap …

We study the problem of generating interconnected questions in question-answering style
conversations. Compared with previous works which generate questions based on a single …

Distractor generation task aims to generate incorrect options (ie, distractors) for multiple
choice questions from an article. Existing methods for this task often utilize a standard …

Automatic question generation (AQG) is the task of generating a question from a given
passage and an answer. Most existing AQG methods aim at encoding the passage and the …

Humans ask follow-up questions driven by curiosity, which reflects a creative human
cognitive process. We introduce the task of real-world information-seeking follow-up …

Abstract Community Question Answering (CQA) has become an indispensable way for
modern people to share and acquire knowledge. It allows users to ask questions, which will …

Existing research for question generation encodes the input text as a sequence of tokens
without explicitly modeling fact information. These models tend to generate irrelevant and …