# Metrical Tagging in the Wild: Building and Annotating Poetry Corpora with Rhythmic Features

arXiv: https://arxiv.org/abs/2102.08858 · HTML: https://mlsanity.com/paper/2102.08858/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 45 references that are in the map.
- First posted on arXiv 17 Feb 2021.

## Builds on (most cited first, 8 of 8)

- [Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging](https://mlsanity.com/paper/1707.09861/) (2017)
- [Shakespearizing Modern Language Using Copy-Enriched Sequence-to-Sequence Models](https://mlsanity.com/paper/1707.01161/) (2017)
- [Deep-speare: A Joint Neural Model of Poetic Language, Meter and Rhyme](https://mlsanity.com/paper/1807.03491/) (2018)
- [PO-EMO: Conceptualization, Annotation, and Modeling of Aesthetic Emotions in German and English Poetry](https://mlsanity.com/paper/2003.07723/) (2020)
- [Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations](https://mlsanity.com/paper/1908.02262/) (2019)
- [Learning Rhyming Constraints using Structured Adversaries](https://mlsanity.com/paper/1909.06743/) (2019)
- [Relative contributions of Shakespeare and Fletcher in Henry VIII: An Analysis Based on Most Frequent Words and Most Frequent Rhythmic Patterns](https://mlsanity.com/paper/1911.05652/) (2019)
- [A Comparison of Feature-Based and Neural Scansion of Poetry](https://mlsanity.com/paper/1711.00938/) (2017)

## Built on by (most cited first, 8 of 8)

- [ByGPT5: End-to-End Style-conditioned Poetry Generation with Token-free Language Models](https://mlsanity.com/paper/2212.10474/) (2022)
- [Sonnet or Not, Bot? Poetry Evaluation for Large Models and Datasets](https://mlsanity.com/paper/2406.18906/) (2024)
- [Aesthetics of Sanskrit Poetry from the Perspective of Computational Linguistics: A Case Study Analysis on Siksastaka](https://mlsanity.com/paper/2308.07081/) (2023)
- [Let the Poem Hit the Rhythm: Using a Byte-Based Transformer for Beat-Aligned Poetry Generation](https://mlsanity.com/paper/2406.10174/) (2024)
- [Automated Evaluation of Meter and Rhyme in Russian Generative and Human-Authored Poetry](https://mlsanity.com/paper/2502.20931/) (2025)
- [Poem Meter Classification of Recited Arabic Poetry: Integrating High-Resource Systems for a Low-Resource Task](https://mlsanity.com/paper/2504.12172/) (2025)
- [PARSI: Persian Authorship Recognition via Stylometric Integration](https://mlsanity.com/paper/2506.21840/) (2025)
- [Computational Social Linguistics for Telugu Cultural Preservation: Novel Algorithms for Chandassu Metrical Pattern Recognition](https://mlsanity.com/paper/2510.01233/) (2025)

## Abstract (arXiv)

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.