A prerequisite for the computational study of literature is the availability
of properly digitized texts, ideally with reliable meta-data and ground-truth
annotation. Poetry corpora do exist for a number of languages, but larger
collections lack consistency and are encoded in various standards, while
annotated corpora are typically constrained to a particular genre and/or were
designed for the analysis of certain linguistic features (like rhyme). In this
work, we provide large poetry corpora for English and German, and annotate
prosodic features in smaller corpora to train corpus driven neural models that
enable robust large scale analysis.
We show that BiLSTM-CRF models with syllable embeddings outperform a CRF
baseline and different BERT-based approaches. In a multi-task setup, particular
beneficial task relations illustrate the inter-dependence of poetic features. A
model learns foot boundaries better when jointly predicting syllable stress,
aesthetic emotions and verse measures benefit from each other, and we find that
caesuras are quite dependent on syntax and also integral to shaping the overall
measure of the line.

本文提供了英语和德语的大型诗歌语料库，并注释了较小语料库中的韵律特征以训练神经模型，以实现鲁棒性大规模分析。我们表明，具有音节嵌入的双向 LSTM-CRF 模型胜过 CRF 基线和不同的 BERT-based 方法。在多任务设置中，特定有益的任务关系说明了诗歌特征的相互依赖。