@inproceedings{ruiz-fabo-etal-2026-automatic,
    title = "Automatic Metrical Scansion of Poetry in a Low-Resource Setting",
    author = "Ruiz Fabo, Pablo  and
      P{\'e}rez, Anxo Alonso  and
      Rodr{\'i}guez Fern{\'a}ndez, Pablo  and
      Gamallo, Pablo",
    editor = "Montejo-Raez, Arturo  and
      Grisot, Cristina  and
      Blochowiak, Joanna  and
      Ljube{\v{s}}i{\'c}, Nikola  and
      Battaner, Elena  and
      Rigau, German",
    booktitle = "Proceedings of Shaping Multilingual, Multimodal {AI} for the Social Sciences and Humanities ({LLM}s4{SSH}) @ {LREC} 2026",
    month = may,
    year = "2026",
    address = "Palma de Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/paragraph-normalization/2026.llms4ssh-1.12/",
    doi = "10.63317/4pzd6u7388jm",
    pages = "114--125",
    abstract = "We present the first neural systems for automatic metrical scansion of poetry in Galician, a Romance language close to Portuguese and Spanish. The task is threefold: First, identifying metrical syllables based on lexical ones; both syllable series may differ given metrical licenses modifying a line{'}s syllable structure to enable stress-related rhythms. Second, identifying stress patterns, and third identifying the metrical syllable count, based on stressed positions. We manually annotated a corpus of 4,287 examples, a first in Galician, and fine-tuned an 8B-parameter LLM specialized in Galician and Portuguese, and two encoder{--}decoder models: ByT5, a token-free byte-to-byte model, and the multilingual mT5, which includes Galician. We also tested our recent symbolic scansion system. Several fine-tuning setups reached exact per-line accuracy above 90{\%} on our test-set at all three scansion subtasks, using orthographic syllables with explicit stress marks as input. Encoder{--}decoders performed better than the LLM. The token-free ByT5 was best, particularly when adding the two surrounding lines to the input. The symbolic system (89.9{\%} acc.) managed rare metaplasms infrequent in training data better than the neural ones, and the approaches can be seen as complementary."
}