@inproceedings{martinez-alonso-plank-2017-multitask,
    title = "When is multitask learning effective? Semantic sequence prediction under varying data conditions",
    author = "Mart{\'i}nez Alonso, H{\'e}ctor  and
      Plank, Barbara",
    editor = "Lapata, Mirella  and
      Blunsom, Phil  and
      Koller, Alexander",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/E17-1005/",
    pages = "44--53",
    abstract = "Multitask learning has been applied successfully to a range of tasks, mostly morphosyntactic. However, little is known on \textit{when} MTL works and whether there are data characteristics that help to determine the success of MTL. In this paper we evaluate a range of semantic sequence labeling tasks in a MTL setup. We examine different auxiliary task configurations, amongst which a novel setup, and correlate their impact to data-dependent conditions. Our results show that MTL is not always effective, because significant improvements are obtained only for 1 out of 5 tasks. When successful, auxiliary tasks with compact and more uniform label distributions are preferable."
}