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Nearest-Neighbor Retrieval for Indigenous Image Captioning

Summary

This NAIST paper presents the team's entry for the AmericasNLP 2026 shared task, which focuses on generating image captions in Indigenous languages. The task covers Bribri, Guaraní, Nahuatl, Wixárika, and Yucatec Maya, all of which have very little training data. The authors compare two systems.

The first is a simple nearest-neighbor method. It embeds the query image with OpenCLIP (ViT-B/32, LAION-2B), finds the closest development-set image by cosine similarity, and reuses that image's caption unchanged. The second is a five-stage pipeline built on GPT models:

  • scene analysis,
  • dictionary-based lexical pruning,
  • retrieval of structurally similar examples,
  • interlinear gloss generation,
  • surface realization, either by the LLM or by a rule-based conversion script.

The pipeline relies on hand-made glossed captions, morpheme alignment tables, and bilingual dictionaries. Retrieval clearly won on chrF++ and placed at or near the top of the leaderboard for several languages. The pipeline produced short, over-constrained captions, hampered by sparse dictionaries and spelling mismatches between resources. Human ratings told a more mixed story: retrieved captions are fluent but can describe the wrong scene. The work shows that retrieval is a strong baseline for low-resource captioning and that surface-overlap metrics have clear limits.

Key points

  • Four retrieval similarity strategies were tested on the dev set: CLIP, DINOv2 (ViT-L/14), and LaBSE embeddings of English or Spanish captions generated by gpt-4o-mini. CLIP performed best for four languages, and DINOv2 edged ahead only for Wixárika (19.83 vs. 19.71 chrF++).

  • The test-set chrF++ scores for the CLIP retrieval system were:

    LanguagechrF++Rank
    Bribri19.371st of 14
    Wixárika19.841st of 14
    Nahuatl20.933rd of 12
    Maya15.805th of 9
    Guaraní19.4118th of 24

  • The gloss pipeline (GPT-5.4) scored far lower, for example 4.66–14.23 chrF++ on the dev set. Its outputs were roughly half the length of the references. It was not finalized for Wixárika.

  • Rule-based gloss conversion helped clearly only for Nahuatl, with an average gain of +4.88 chrF++ and 43 of 50 examples improved. It gave a small gain for Bribri and slight average losses for Maya and Guaraní.

  • After human evaluation, the retrieval system ranked 1st for Wixárika (average rating 3.79), 3rd for Bribri, and 4th for Maya, Guaraní, and Nahuatl. Nahuatl's average rating was only 1.22, which shows the gap between chrF++ scores and human judgments of caption accuracy.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.americasnlp-6.26

@inproceedings{vasselli2026nearest,
  title = {{Nearest-Neighbor Retrieval for Indigenous Image Captioning}},
  author = {Vasselli, Justin and Peguero, Arturo Martínez and Ozaki, Shintaro and Hudi, Frederikus and Sakajo, Haruki and Watanabe, Taro},
  booktitle = {Proceedings of the Sixth Workshop on NLP for Indigenous Languages of the Americas (AmericasNLP)},
  year = {2026},
  doi = {10.18653/v1/2026.americasnlp-6.26},
  url = {https://doi.org/10.18653/v1/2026.americasnlp-6.26}
}

References

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