@inproceedings{hosseini-kivanani-2026-polyframe,
    title = "{P}oly{F}rame at {MWE}-2026 {A}d{MIR}e 2: When Words Are Not Enough: Multimodal Idiom Disambiguation",
    author = "Hosseini-Kivanani, Nina",
    editor = {Ojha, Atul Kr.  and
      Mititelu, Verginica Barbu  and
      Constant, Mathieu  and
      Stoyanova, Ivelina  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Rademaker, Alexandre},
    booktitle = "Proceedings of the 22nd Workshop on Multiword Expressions ({MWE} 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Marocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.mwe-1.15/",
    pages = "127--133",
    ISBN = "979-8-89176-363-0",
    abstract = "Multimodal models struggle with idiomatic expressions due to their non-compositional meanings, a challenge amplified in multilingual settings. We introduced PolyFrame, our system for the MWE-2026 AdMIRe 2 shared task on multimodal idiom disambiguation, featuring a unified pipeline for both image+text ranking (Subtask A) and text-only caption ranking (Subtask B). All model variants retain frozen CLIP-style vision{--}language encoders and the multilingual BGE{~}M3 encoder, training only lightweight modules: a logistic regression and LLM-based sentence-type predictor, idiom synonym substitution, distractor-aware scoring, and Borda rank fusion. Starting from a CLIP baseline (26.7{\%} Top-1 on English dev, 6.7{\%} on English test), adding idiom-aware paraphrasing and explicit sentence-type classification increased performance to 60.0{\%} Top-1 on English, and 60.0{\%} Top-1 (0.822 NDCG@5) in zero-shot transfer to Portuguese. On the multilingual blind test, our systems achieved average Top-1/NDCG scores of 0.35/0.73 for Subtask{~}A and 0.32/0.71 for Subtask{~}B across 15 languages. Ablation results highlight idiom-aware rewriting as the main contributor to performance, while sentence-type prediction and multimodal fusion enhance robustness. These findings suggest that effective idiom disambiguation is feasible without fine-tuning large multimodal encoders."
}