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- W4383468861 abstract "The computational analysis of poetry is limited by the scarcity of tools to automatically analyze and scan poems. In a multilingual settings, the problem is exacerbated as scansion and rhyme systems only exist for individual languages, making comparative studies very challenging and time consuming. In this work, we present textsc{Alberti}, the first multilingual pre-trained large language model for poetry. Through domain-specific pre-training (DSP), we further trained multilingual BERT on a corpus of over 12 million verses from 12 languages. We evaluated its performance on two structural poetry tasks: Spanish stanza type classification, and metrical pattern prediction for Spanish, English and German. In both cases, textsc{Alberti} outperforms multilingual BERT and other transformers-based models of similar sizes, and even achieves state-of-the-art results for German when compared to rule-based systems, demonstrating the feasibility and effectiveness of DSP in the poetry domain." @default.
- W4383468861 created "2023-07-07" @default.
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- W4383468861 date "2023-07-03" @default.
- W4383468861 modified "2023-09-23" @default.
- W4383468861 title "ALBERTI, a Multilingual Domain Specific Language Model for Poetry Analysis" @default.
- W4383468861 doi "https://doi.org/10.48550/arxiv.2307.01387" @default.
- W4383468861 hasPublicationYear "2023" @default.
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