@InProceedings{elsahar-gravier-laforest:2018:N18-1,
  author    = {Elsahar, Hady  and  Gravier, Christophe  and  Laforest, Frederique},
  title     = {Zero-Shot Question Generation from Knowledge Graphs for Unseen Predicates and Entity Types},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {218--228},
  abstract  = {We present a neural model for question generation from knowledge graphs triples in a "Zero-shot" setup, that is generating questions for predicate, subject types or object types that were not seen at training time. Our model leverages triples occurrences in the natural language corpus in a encoder-decoder architecture, paired with an original part-of-speech copy action mechanism to generate questions. Benchmark and human evaluation show that our model outperforms state-of-the-art on this task.},
  url       = {http://www.aclweb.org/anthology/N18-1020}
}