@inproceedings{bollmann-etal-2018-multi,
    title = "Multi-task learning for historical text normalization: Size matters",
    author = "Bollmann, Marcel  and
      S{\o}gaard, Anders  and
      Bingel, Joachim",
    editor = "Haffari, Reza  and
      Cherry, Colin  and
      Foster, George  and
      Khadivi, Shahram  and
      Salehi, Bahar",
    booktitle = "Proceedings of the Workshop on Deep Learning Approaches for Low-Resource {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/W18-3403/",
    doi = "10.18653/v1/W18-3403",
    pages = "19--24",
    abstract = "Historical text normalization suffers from small datasets that exhibit high variance, and previous work has shown that multi-task learning can be used to leverage data from related problems in order to obtain more robust models. Previous work has been limited to datasets from a specific language and a specific historical period, and it is not clear whether results generalize. It therefore remains an open problem, when historical text normalization benefits from multi-task learning. We explore the benefits of multi-task learning across 10 different datasets, representing different languages and periods. Our main finding{---}contrary to what has been observed for other NLP tasks{---}is that multi-task learning mainly works when target task data is very scarce."
}