A Comparison of Feature-Based and Neural Scansion of Poetry
This work addresses a domain-specific problem in computational linguistics and literature, providing incremental improvements in scansion accuracy.
The paper tackled automatic poetic rhythm analysis in English and Spanish, showing that character-based neural models, specifically a Bi-LSTM+CRF model, achieve state-of-the-art accuracy for scansion in both languages.
Automatic analysis of poetic rhythm is a challenging task that involves linguistics, literature, and computer science. When the language to be analyzed is known, rule-based systems or data-driven methods can be used. In this paper, we analyze poetic rhythm in English and Spanish. We show that the representations of data learned from character-based neural models are more informative than the ones from hand-crafted features, and that a Bi-LSTM+CRF-model produces state-of-the art accuracy on scansion of poetry in two languages. Results also show that the information about whole word structure, and not just independent syllables, is highly informative for performing scansion.