@inproceedings{hong-etal-2020-handling,
    title = "Handling Anomalies of Synthetic Questions in Unsupervised Question Answering",
    author = "Hong, Giwon  and
      Kang, Junmo  and
      Lim, Doyeon  and
      Myaeng, Sung-Hyon",
    editor = "Scott, Donia  and
      Bel, Nuria  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.coling-main.306/",
    doi = "10.18653/v1/2020.coling-main.306",
    pages = "3441--3448",
    abstract = "Advances in Question Answering (QA) research require additional datasets for new domains, languages, and types of questions, as well as for performance increases. Human creation of a QA dataset like SQuAD, however, is expensive. As an alternative, an unsupervised QA approach has been proposed so that QA training data can be generated automatically. However, the performance of unsupervised QA is much lower than that of supervised QA models. We identify two anomalies in the automatically generated questions and propose how they can be mitigated. We show our approach helps improve unsupervised QA significantly across a number of QA tasks."
}