@inproceedings{shijia-etal-2026-fl,
    title = "{FL}-{MSCL}: A Unified Figurative Language Detection Model Driven by Multi-Type Signals and Contrastive Learning",
    author = "Shijia, Lu  and
      Fukumoto, Fumiyo  and
      Xiaoxi, Huang  and
      Suzuki, Yoshimi",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-short.57/",
    pages = "694--702",
    ISBN = "979-8-89176-391-3",
    abstract = "Figurative language recognition poses significant challenges in NLP, particularly when distinguishing between fine-grained rhetorical categories such as metaphor, metonymy, and simile. This paper formulates the problem as a four-way sentence-level classification task and proposes FL-MSCL, a unified framework integrating prompt-based knowledge injection with supervised contrastive learning. Experiments across both unified and single-class benchmarks demonstrate that FL-MSCL achieves competitive performance compared to State-of-the-Art (SOTA) methods, indicating consistent advantages in cross-category generalization and category-specific detection."
}