@inproceedings{mao-etal-2020-tchebycheff,
    title = "Tchebycheff Procedure for Multi-task Text Classification",
    author = "Mao, Yuren  and
      Yun, Shuang  and
      Liu, Weiwei  and
      Du, Bo",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.acl-main.388/",
    doi = "10.18653/v1/2020.acl-main.388",
    pages = "4217--4226",
    abstract = "Multi-task Learning methods have achieved great progress in text classification. However, existing methods assume that multi-task text classification problems are convex multiobjective optimization problems, which is unrealistic in real-world applications. To address this issue, this paper presents a novel Tchebycheff procedure to optimize the multi-task classification problems without convex assumption. The extensive experiments back up our theoretical analysis and validate the superiority of our proposals."
}