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Poem Meter Classification of Recited Arabic Poetry: Integrating High-Resource Systems for a Low-Resource Task

Summary

The paper tackles automatic identification of the meter (the rhythmic pattern studied in Arabic prosody, Arud) of Arabic poetry when the input is recorded recitation rather than written text. Labelled recited verse is scarce, so a model trained directly on audio has little to learn from. The authors compare two Wav2Vec2-based designs. The first maps sound straight to a meter label. The second is a pipeline: a speech model fine-tuned on poetry transcribes the verse, and an existing text-based meter classifier, trained on large written corpora, labels the transcript. The direct audio model performed poorly because there was too little training data. The pipeline produced state-of-the-art results, with F1 of 80% on a private Baseline set and 87% on a newly released public Benchmark. An ablation with gold transcripts shows how much of the error comes from each stage. The work matters because it shows a low-resource task can be solved by joining two components that each benefit from abundant data, and because the public benchmark gives future research a shared test set.

Key points

  • Arabic classical poetry has 16 meters. Earlier acoustic work (Al-Talabani) covered only 3 meters and used 230 verses with an LSTM.
  • Both systems build on Wav2Vec2. The transcription step compares Wav2Vec-Base with Wav2Vec-FineTuned, and the text step uses classifiers such as the one from Abandah et al. Related textual work relies on the MetRec dataset (55,400 verses, 14 meters).
  • The best pipeline reaches F1 80% (Baseline) and 87% (Benchmark). The end-to-end audio classifier lagged clearly behind.
  • With perfect transcriptions, F1 rises to 87% on Baseline and 89% on Benchmark (accuracy 89% and 95%). Transcription errors therefore cost about 7 F1 points on Baseline and about 2 points on Benchmark.
  • Meters are still recognised robustly even when WER exceeds 10% and CER sits at 2–5%. Rare but similar meters (Mudhare vs. Mujtath) are confused, while frequent meters are almost always correct.

Interesting ideas

  • I1. Instead of training one model end to end on tiny data, route the task through an intermediate representation (text) that lets two separately data-rich systems be chained.
  • I2. An oracle ablation feeds gold intermediate output into the downstream stage, splitting total error into upstream and downstream shares.
  • I3. The downstream classifier tolerated quite noisy transcripts, which suggests that measuring robustness to upstream noise is as important as measuring upstream accuracy.
  • I4. Fine-tuning the speech model on poetry seemed to make it treat poetry less like prose, which points to domain adaptation of the upstream component.
  • I5. The confusion analysis showed that errors cluster in rare classes that are close to more frequent neighbours.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (learn): When a target task has few labels, Mova can decompose it into stages that each have rich training sources and connect them through explicit, inspectable intermediate structures such as UD trees and frames.
  • I2 (learn): Mova's evaluation could run frames and world building on gold UD trees as well as predicted ones, attributing each lost point to the parser or to the later stages.
  • I3 (frames): Feed deliberately degraded or alternative parses into crate prag and check whether the who-did-what-to-whom frames stay stable, which shows where repair effort actually pays off.
  • I4 (tree): gparse could be fine-tuned on in-genre text such as fables or school texts, then checked for fewer genre-specific attachment errors that propagate into later stages.
  • I5 (world): When assigning VerbNet classes or ontology types, inspect confusions between rare, similar classes and add targeted silver data or explicit discriminating rules for those pairs.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Topics Low-resource languages Semitic languages

 Institutions

🎓King Fahd University of Petroleum and MineralsDhahran · Saudi Arabia23 works · 4,250 citations

Cite

Cite as Maged S. Al-Shaibani, Zaid Alyafeai and Irfan Ahmad. 2025. Poem Meter Classification of Recited Arabic Poetry: Integrating High-Resource Systems for a Low-Resource Task. arXiv:2504.12172.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2504.12172 · arXiv 2504.12172

@misc{alshaibani2025poem,
  title = {{Poem Meter Classification of Recited Arabic Poetry: Integrating High-Resource Systems for a Low-Resource Task}},
  author = {Maged S. Al-Shaibani, Zaid Alyafeai and Irfan Ahmad},
  year = {2025},
  doi = {10.48550/arxiv.2504.12172},
  eprint = {2504.12172},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2504.12172}
}

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