@inproceedings{karl-scherp-2025-hydra,
    title = "{HYDRA}: A Multi-Head Encoder-only Architecture for Hierarchical Text Classification",
    author = "Karl, Fabian  and
      Scherp, Ansgar",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.472/",
    pages = "9303--9314",
    ISBN = "979-8-89176-332-6",
    abstract = "We introduce HYDRA, a simple yet effective multi-head encoder-only architecture for hierarchical text classification that treats each level in the hierarchy as a separate classification task with its own label space. State-of-the-art approaches rely on complex components like graph encoders, label semantics, and autoregressive decoders. We demonstrate that such complexity is often unnecessary. Through parameter sharing and level-specific parameterization, HYDRA enables flat models to incorporate hierarchical awareness without architectural complexity. Experiments on four benchmarks (NYT, RCV1-V2, BGC, and WOS) demonstrate that HYDRA always increases the performance over flat models and matches or exceeds the performance of complex state-of-the-art methods."
}