@inproceedings{wang-etal-2022-ranking,
    title = "Ranking-Constrained Learning with Rationales for Text Classification",
    author = "Wang, Juanyan  and
      Sharma, Manali  and
      Bilgic, Mustafa",
    editor = "Muresan, Smaranda  and
      Nakov, Preslav  and
      Villavicencio, Aline",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2022.findings-acl.161/",
    doi = "10.18653/v1/2022.findings-acl.161",
    pages = "2034--2046",
    abstract = "We propose a novel approach that jointly utilizes the labels and elicited rationales for text classification to speed up the training of deep learning models with limited training data. We define and optimize a ranking-constrained loss function that combines cross-entropy loss with ranking losses as rationale constraints. We evaluate our proposed rationale-augmented learning approach on three human-annotated datasets, and show that our approach provides significant improvements over classification approaches that do not utilize rationales as well as other state-of-the-art rationale-augmented baselines."
}