@InProceedings{song-EtAl:2018:N18-22,
  author    = {Song, Linfeng  and  Wang, Zhiguo  and  Hamza, Wael  and  Zhang, Yue  and  Gildea, Daniel},
  title     = {Leveraging Context Information for Natural Question Generation},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {569--574},
  abstract  = {The task of natural question generation is to generate a corresponding question given the input passage (fact) and answer. It is useful for enlarging the training set of QA systems. Previous work has adopted sequence-to-sequence models that take a passage with an additional bit to indicate answer position as input. However, they do not explicitly model the information between answer and other context within the passage. We propose a model that matches the answer with the passage before generating the question. Experiments show that our model outperforms the existing state of the art using rich features.},
  url       = {http://www.aclweb.org/anthology/N18-2090}
}