@inproceedings{vasselli-etal-2026-nearest,
    title = "Nearest-Neighbor Retrieval for Indigenous Image Captioning",
    author = "Vasselli, Justin  and
      Mart{\'i}nez Peguero, Arturo  and
      Ozaki, Shintaro  and
      Hudi, Frederikus  and
      Sakajo, Haruki  and
      Watanabe, Taro",
    editor = "Mager, Manuel  and
      Ebrahimi, Abteen  and
      Bui, Minh Duc  and
      Pugh, Robert  and
      Oncevay, Arturo  and
      Chiruzzo, Luis  and
      Solano, Rolando Coto  and
      Rijhwani, Shruti  and
      Von Der Wense, Katharina",
    booktitle = "Proceedings of the Sixth Workshop on {NLP} for Indigenous Languages of the {A}mericas ({A}mericas{NLP})",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.americasnlp-6.26/",
    pages = "272--278",
    ISBN = "979-8-89176-415-6",
    abstract = "This paper describes the NAIST submission to the AmericasNLP 2026 Shared Task on Indigenous Language Image Captioning. We investigate two approaches for generating captions in Bribri, Guaran{\'i}, Nahuatl, Wix{\'a}rika, and Yucatec Maya. The first is a nearest-neighbor retrieval system that uses CLIP image embeddings to retrieve the most similar image from the development set and directly reuse its caption. The second is a generation pipeline that combines scene analysis, dictionary-grounded lexical planning, retrieved gloss templates, and interlinear gloss representations to constrain generation in low-resource settings.The retrieval-based approach substantially outperformed the gloss-based pipeline under chrF++ evaluation and was competitive across all submitted systems, achieving first-place automated system rankings for Bribri and Wix{\'a}rika and third place for Nahuatl. The gloss-based pipeline produced weaker automatic evaluation results and exposed problems with dictionary coverage, orthographic mismatches between resources, and unstable grammatical generation. Our results suggest that retrieval-based methods provide a strong baseline for low-resource captioning tasks when high-quality examples are available."
}