@inproceedings{elsahar-etal-2018-zero,
    title = "Zero-Shot Question Generation from Knowledge Graphs for Unseen Predicates and Entity Types",
    author = "Elsahar, Hady  and
      Gravier, Christophe  and
      Laforest, Frederique",
    editor = "Walker, Marilyn  and
      Ji, Heng  and
      Stent, Amanda",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/N18-1020/",
    doi = "10.18653/v1/N18-1020",
    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."
}