@inproceedings{pommeret-etal-2026-thivlvc,
    title = "{THIVLVC}: Retrieval Augmented Dependency Parsing for {L}atin",
    author = "Pommeret, Luc  and
      Wagret, Thibault  and
      Deret, Jules",
    editor = "Sprugnoli, Rachele  and
      Passarotti, Marco",
    booktitle = "Proceedings of the Fourth Workshop on Language Technologies for Historical and Ancient Languages ({LT}4{HALA} 2026) @ {LREC} 2026",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/ingest-nlpsi/2026.lt4hala-1.20/",
    doi = "10.63317/2q8twojtotyb",
    pages = "219--225",
    abstract = "We describe THIVLVC, a two-stage system for the EvaLatin 2026 Dependency Parsing task. Given a Latin sentence, we retrieve structurally similar entries from the CIRCSE treebank using sentence length and POS $n$-gram similarity, then prompt a large language model to refine the baseline parse from UDPipe using the retrieved examples and UD annotation guidelines. We submit two configurations: one without retrieval and one with retrieval (RAG). On poetry (Seneca), THIVLVC improves CLAS by +17 points over the UDPipe baseline; on prose (Thomas Aquinas), the gain is +1.5 CLAS. A double-blind error analysis of 300 divergences between our system and the gold standard reveals that, among unanimous annotator decisions, 53.3{\%} favour THIVLVC, showing annotation inconsistencies both within and across treebanks."
}