@inproceedings{ozer-etal-2026-interlinear,
    title = "Interlinear Glosses as a Multilingual Pivot for Machine Translation: An Updated Study on {T}urkish with Restricted Resources",
    author = "Ozer, Volkan  and
      Okabe, Shu  and
      Fraser, Alexander",
    editor = "Ojha, Atul Kr.  and
      Sakti, Sakriani  and
      Soria, Claudia  and
      Melero, Maite  and
      McCrae, John P.  and
      Lignos, Constantine  and
      Liu, Chao-Hong  and
      Claramunt, German Rigau  and
      Rehm, Georg",
    booktitle = "Proceedings of the {SIGUL} 2026 Joint Workshop with {ELE}, {EURALI}, and {DCLRL}: Towards Inclusivity and Equality: Language Resources and Technologies for Under-Resourced and Endangered Languages",
    month = may,
    year = "2026",
    address = "Palma, Mallorca, Spain",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/revision-workflow/2026.sigul-1.19/",
    doi = "10.63317/4km7f5p8epxt",
    pages = "183--197",
    abstract = "Translating very low-resource languages is a challenge that has been approached using available linguistic cues. Among them, interlinear glosses are linguistic annotations that can essentially bridge the gap between two languages thanks to both grammatical and lexical information. We perform a case study on a simulated low-resource condition for Turkish, a morphologically rich language, with a pipeline approach, following (Zhou et al., 2020). A source sentence is passed through a morphological analyzer and a bilingual dictionary to obtain a gloss-like representation. We then evaluate the current capacity of Neural Machine Translation systems and Large Language Models in performing the translation task from interlinear glosses into fluent English translations. We notably evaluate how performance scales with multilingual glossed data and how translation is affected by pseudo-glosses. Pivoting with glosses remains a better approach than a direct translation for languages with limited parallel data for training. Although glosses remain helpful resources, translations are sensitive to their quality, especially for lexical information."
}