@inproceedings{meshgi-etal-2022-q,
    title = "{Q}-Learning Scheduler for Multi Task Learning Through the use of Histogram of Task Uncertainty",
    author = "Meshgi, Kourosh  and
      Sadat Mirzaei, Maryam  and
      Sekine, Satoshi",
    editor = "Gella, Spandana  and
      He, He  and
      Majumder, Bodhisattwa Prasad  and
      Can, Burcu  and
      Giunchiglia, Eleonora  and
      Cahyawijaya, Samuel  and
      Min, Sewon  and
      Mozes, Maximilian  and
      Li, Xiang Lorraine  and
      Augenstein, Isabelle  and
      Rogers, Anna  and
      Cho, Kyunghyun  and
      Grefenstette, Edward  and
      Rimell, Laura  and
      Dyer, Chris",
    booktitle = "Proceedings of the 7th Workshop on Representation Learning for NLP",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.repl4nlp-1.2",
    doi = "10.18653/v1/2022.repl4nlp-1.2",
    pages = "9--19",
    abstract = "Simultaneous training of a multi-task learning network on different domains or tasks is not always straightforward. It could lead to inferior performance or generalization compared to the corresponding single-task networks. An effective training scheduling method is deemed necessary to maximize the benefits of multi-task learning. Traditional schedulers follow a heuristic or prefixed strategy, ignoring the relation of the tasks, their sample complexities, and the state of the emergent shared features. We proposed a deep Q-Learning Scheduler (QLS) that monitors the state of the tasks and the shared features using a novel histogram of task uncertainty, and through trial-and-error, learns an optimal policy for task scheduling. Extensive experiments on multi-domain and multi-task settings with various task difficulty profiles have been conducted, the proposed method is benchmarked against other schedulers, its superior performance has been demonstrated, and results are discussed.",
}