@inproceedings{harrison-walker-2018-neural,
    title = "Neural Generation of Diverse Questions using Answer Focus, Contextual and Linguistic Features",
    author = "Harrison, Vrindavan  and
      Walker, Marilyn",
    editor = "Krahmer, Emiel  and
      Gatt, Albert  and
      Goudbeek, Martijn",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/W18-6536/",
    doi = "10.18653/v1/W18-6536",
    pages = "296--306",
    abstract = "Question Generation is the task of automatically creating questions from textual input. In this work we present a new Attentional Encoder{--}Decoder Recurrent Neural Network model for automatic question generation. Our model incorporates linguistic features and an additional sentence embedding to capture meaning at both sentence and word levels. The linguistic features are designed to capture information related to named entity recognition, word case, and entity coreference resolution. In addition our model uses a copying mechanism and a special answer signal that enables generation of numerous diverse questions on a given sentence. Our model achieves state of the art results of 19.98 Bleu{\_}4 on a benchmark Question Generation dataset, outperforming all previously published results by a significant margin. A human evaluation also shows that the added features improve the quality of the generated questions."
}