@inproceedings{wu-huang-2022-unsupervised,
    title = "Unsupervised Reinforcement Adaptation for Class-Imbalanced Text Classification",
    author = "Wu, Yuexin  and
      Huang, Xiaolei",
    editor = "Nastase, Vivi  and
      Pavlick, Ellie  and
      Pilehvar, Mohammad Taher  and
      Camacho-Collados, Jose  and
      Raganato, Alessandro",
    booktitle = "Proceedings of the 11th Joint Conference on Lexical and Computational Semantics",
    month = jul,
    year = "2022",
    address = "Seattle, Washington",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.starsem-1.27/",
    doi = "10.18653/v1/2022.starsem-1.27",
    pages = "311--322",
    abstract = "Class imbalance naturally exists when label distributions are not aligned across source and target domains. However, existing state-of-the-art UDA models learn domain-invariant representations across domains and evaluate primarily on class-balanced data. In this work, we propose an unsupervised domain adaptation approach via reinforcement learning that jointly leverages feature variants and imbalanced labels across domains. We experiment with the text classification task for its easily accessible datasets and compare the proposed method with five baselines. Experiments on three datasets prove that our proposed method can effectively learn robust domain-invariant representations and successfully adapt text classifiers on imbalanced classes over domains."
}