---
title: "Active Learning for Multidialectal Arabic POS Tagging — Akra 2025"
description: "Building POS taggers for Arabic dialects is hard. 97.6% accuracy. 63 references, cited by 2 papers here."
authors: ["Diyam Akra", "Mohammed Khalilia", "Mustafa Jarrar"]
year: 2025
venue: "Findings of the Association for Computational Linguistics: EMNLP 2025"
source: "https://aclanthology.org/2025.findings-emnlp.1359/"
doi: "10.18653/v1/2025.findings-emnlp.1359"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/akra-2025-active-learning-multidialectal-arabic-pos/"
---
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# Active Learning for Multidialectal Arabic POS Tagging

**Authors:** [Diyam Akra](https://md.research.elamur.ai/free/en/authors/diyam-akra/), [Mohammed Khalilia](https://md.research.elamur.ai/free/en/authors/mohammed-khalilia/) and [Mustafa Jarrar](https://md.research.elamur.ai/free/en/authors/mustafa-jarrar/) · **Year:** 2025 · **Venue:** [Findings of the Association for Computational Linguistics: EMNLP 2025](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2025](https://md.research.elamur.ai/free/en/venues/findings/2025/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2025.findings-emnlp.1359/) · [PDF](https://aclanthology.org/2025.findings-emnlp.1359.pdf) · [DOI 10.18653/v1/2025.findings-emnlp.1359](https://doi.org/10.18653/v1/2025.findings-emnlp.1359) · [OpenAlex](https://openalex.org/W4416034429)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [2](https://md.research.elamur.ai/free/en/papers/akra-2025-active-learning-multidialectal-arabic-pos/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Building POS taggers for Arabic dialects is hard. The morphology is rich, the dialects vary widely, and manually annotated dialectal corpora take teams of annotators years to produce. This work asks whether active learning can cut that annotation burden substantially. The authors work with MSA and four dialects: Palestinian, Syrian, Egyptian and Emirati. They draw on seven corpora (ATB, SALMA, ARZTB, Curras, Nabra, Baladi, Gumar) and map them all onto the ALMA tagset.

Across 29 experiments they vary four parts of the active learning loop:
- the pretrained BERT model,
- how the initial seed sample is chosen,
- whether annotation is done per sentence or per token,
- the query strategy.

Selecting informative tokens with an uncertainty strategy makes accuracy level off at about 15,000 annotated tokens. Using a tagger already fine-tuned on a different dialect to choose the seed batch speeds up convergence further. The resulting pipeline was validated on the Emirati corpus Gumar, where it reached 97.6% accuracy. For under-resourced dialects, this suggests that a fairly small, well-chosen annotation effort can produce a usable tagger.

## Key points
- Tags were harmonized in two steps. First, labels that differed only in naming were unified (e.g., PRON_DEM became DEM_PRON). Second, tokens tagged inconsistently across corpora were mapped by value (e.g., digits labeled NOUN_NUM became DIGIT). SALMA was held out as a test-only set.
- Three Arabic BERT variants were compared on Curras: AraBERT (twitter v02), MARBERTV2 and CAMeLBERT-Mix. Selection was done at the token level rather than the sentence level.
- With about 15,000 selected tokens, accuracy reached 97.3% (MSA), 95.5% (Palestinian), 94.8% (Egyptian) and 92.9% (Syrian). The Syrian corpus lagged because of rare and ambiguous words.
- Breaking Ties, Least Confidence and Entropy performed similarly well, and Breaking Ties converged soonest. Seeding from a model fine-tuned on another dialect added 2.5–5% accuracy in the first round and saved roughly 2,000 tokens of annotation.
- The final pipeline has three steps:
  1. Choose the seed batch with a cross-dialect model.
  2. Query 2,000 tokens per round using Breaking Ties.
  3. Stop at 16,000 tokens.
  
  On Gumar (Emirati) this reached 97.6%.

## Interesting ideas
- Select individual uncertain tokens instead of whole sentences, so that annotation effort is not spent on predictable function words.
- The Breaking Ties criterion ranks items by how small the gap is between the model's top two candidates, and it surfaced hard cases earlier than entropy-style scores.
- Warm-start the seed selection with a model trained on a related variety, so that the very first labeled batch already targets that model's blind spots.
- At each round, evaluate on held-out corpora the model never trains on, to confirm that gains are not overfitting to the selection pool.
- Split tagset harmonization into renaming equivalent labels and manually remapping tokens whose tags genuinely conflict.

## For Mova

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

- **I1** (learn): When escalating to an LLM teacher tier, ask only about the specific tokens or arcs where the witness parsers disagree, rather than sending the whole sentence.
- **I2** (tokens): The en tagger could log the top-two margin for each token and queue the narrowest-margin tokens for repair rules or teacher review.
- **I3** (tree): A gparse model trained on standard English UD treebanks could pick the seed sentences when Mova adapts to a new register, such as fables or school-test prose.
- **I4** (learn): This fits the negative-control evaluation, by tracking each silver-data iteration against untouched out-of-genre sets as well as the in-domain test.
- **I5** (tree): The same two-pass design could structure UD dialect conversion, keeping trivial relabeling separate from the decisions that need linguistic judgment.

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

## Institutions

- [Hamad bin Khalifa University](https://md.research.elamur.ai/free/en/universities/hamad-bin-khalifa-university/) — University, Doha, Qatar · 141 works · 14,175 citations
- [Birzeit University](https://md.research.elamur.ai/free/en/universities/birzeit-university/) — University, Birzeit, Palestine · 36 works · 798 citations

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## Cite

Cite as: Diyam Akra, Mohammed Khalilia and Mustafa Jarrar. 2025. Active Learning for Multidialectal Arabic POS Tagging. Findings of the Association for Computational Linguistics: EMNLP 2025.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{akra2025active,
  title = {{Active Learning for Multidialectal Arabic POS Tagging}},
  author = {Diyam Akra, Mohammed Khalilia and Mustafa Jarrar},
  booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2025},
  year = {2025},
  doi = {10.18653/v1/2025.findings-emnlp.1359},
  url = {https://doi.org/10.18653/v1/2025.findings-emnlp.1359}
}
```

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*Updated 1 November 2026*