Emerging research in Neural Question Generation (NQG) has started to integrate a larger variety of inputs, and generating questions requiring higher levels of cognition. These trends point to NQG as a bellwether for NLP, about how human intelligence embodies the skills of curiosity and integration. We present a comprehensive survey of neural question generation, examining the corpora, methodologies, and evaluation methods. From this, we elaborate on what we see as emerging on NQG's trend: in terms of the learning paradigms, input modalities, and cognitive levels considered by NQG. We end by pointing out the potential directions ahead.
@article{arxiv.1905.08949,
title = {Recent Advances in Neural Question Generation},
author = {Liangming Pan and Wenqiang Lei and Tat-Seng Chua and Min-Yen Kan},
journal= {arXiv preprint arXiv:1905.08949},
year = {2019}
}