Automatic Metrical Scansion of Poetry in a Low-Resource Setting

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.

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