ALBERTI, a Multilingual Domain Specific Language Model for Poetry Analysis

• URL: http://arxiv.org/abs/2307.01387v1
• Date: Mon, 3 Jul 2023 22:50:53 GMT
• Title: ALBERTI, a Multilingual Domain Specific Language Model for Poetry Analysis
• Authors: Javier de la Rosa, \'Alvaro P\'erez Pozo, Salvador Ros, Elena Gonz\'alez-Blanco
• Abstract summary: We present textscAlberti, the first multilingual pre-trained large language model for poetry. We further trained multilingual BERT on a corpus of over 12 million verses from 12 languages. textscAlberti achieves state-of-the-art results for German when compared to rule-based systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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