@inproceedings{yang-etal-2026-learnable,
    title = "A Learnable Skill Combination Strategy for Multi-task Learning in Natural Language Understanding",
    author = "Yang, Zhe  and
      Huang, Yi  and
      Chen, Yaqin  and
      Guo, Mengfei  and
      Wu, Xiaoting  and
      Feng, Junlan",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1886/",
    pages = "37839--37845",
    ISBN = "979-8-89176-395-1",
    abstract = "In the realm of domain-specific natural language understanding (NLU) tasks, acquiring high-quality labeled data is often arduous, thereby posing significant challenges for effective model training. Multi-task learning (MTL) addresses these limitations by jointly optimizing multiple tasks within a unified framework. In this paper, we introduce a novel sparse NLU multi-task learning framework that decomposes the language model into modular skill components and employs a dynamic, learnable skill-combination mechanism to adaptively handle diverse tasks. Extensive experiments on benchmark NLU datasets demonstrate that our proposed method surpasses conventional multi-task learning approaches in performance."
}