@inproceedings{purushothama-etal-2026-syntax,
    title = "Syntax as a Rosetta Stone: {U}niversal {D}ependencies for In-Context {C}optic Translation",
    author = "Purushothama, Abhishek  and
      Thronson, Emma  and
      Guo, Alexia  and
      Zeldes, Amir",
    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.1803/",
    pages = "36176--36195",
    ISBN = "979-8-89176-395-1",
    abstract = "This paper proposes a novel in-context learning approach to support low resource machine translation for the Coptic language, using prompts based on Universal Dependencies parses of input sentences. Building on existing work using bilingual dictionaries to support inference for vocabulary items, we add several representations of syntactic analyses to our inputs, specifically exploring the inclusion of raw parser outputs, verbalizations of parses in plain English, and explanations of specific difficult constructions identified in input subgraphs and how they can be translated. Our results show that while syntactic information alone is not as useful as dictionary-based glosses, combining retrieved dictionary items with syntactic information achieves significant gains across model sizes, achieving new state-of-the-art results for the language."
}