@inproceedings{huang-etal-2026-cmig,
    title = "{CMIG}: Conceptual Metaphor Theory-Inspired Framework for Metaphorical Image Generation",
    author = "Huang, Qingbao  and
      Yang, Cheng  and
      Yao, Jiawei  and
      Liu, Zhiyue  and
      Cai, Yi  and
      Zhang, Xingmao",
    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.1189/",
    pages = "23748--23761",
    ISBN = "979-8-89176-395-1",
    abstract = "Metaphorical text expresses meaning through cross-domain mappings rather than literal surface content, which makes it difficult for text-to-image systems to generate semantically faithful images. We propose CMIG, a structured prompting framework inspired by Conceptual Metaphor Theory (CMT). CMIG identifies source{--}target mappings, filters projectable source attributes, and selects a visual realization strategy in a reproducible reasoning workflow. Experiments on DALL$\cdot$E{~}3, Imagen 2, and FLUX-1 show that CMIG consistently improves semantic alignment and yields a better overall balance of human-rated metaphor quality, visual coherence, and controllability on metaphorical prompts. To support systematic evaluation, we also construct a 3{,}500-instance visual metaphor benchmark."
}