---
title: "NADI 2025 — paper by Talafha et al. (2025) — summary and ideas"
description: "NADI 2025 is the sixth edition of the Nuanced Arabic Dialect Identification series. 79.8% accuracy. 91 references, cited by 14 papers here."
authors: ["Bashar Talafha", "Hawau Olamide Toyin", "Peter Sullivan", "AbdelRahim A. Elmadany", "Abdurrahman Juma", "Amirbek Djanibekov", "Chiyu Zhang", "Hamad Alshehhi", "Hanan Aldarmaki", "Mustafa Jarrar", "Nizar Habash", "Muhammad Abdul-Mageed"]
year: 2025
venue: "Proceedings of The Third Arabic Natural Language Processing Conference: Shared Tasks"
source: "https://aclanthology.org/2025.arabicnlp-sharedtasks.99/"
doi: "10.18653/v1/2025.arabicnlp-sharedtasks.99"
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/talafha-2025-nadi-2025-first-multidialectal-arabic/"
---
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# NADI 2025: The First Multidialectal Arabic Speech Processing Shared Task

**Authors:** [Bashar Talafha](https://md.research.elamur.ai/free/en/authors/bashar-talafha/), [Hawau Olamide Toyin](https://md.research.elamur.ai/free/en/authors/hawau-olamide-toyin/), [Peter Sullivan](https://md.research.elamur.ai/free/en/authors/peter-sullivan/), [AbdelRahim A. Elmadany](https://md.research.elamur.ai/free/en/authors/abdelrahim-elmadany/), [Abdurrahman Juma](https://md.research.elamur.ai/free/en/authors/abdurrahman-juma/), [Amirbek Djanibekov](https://md.research.elamur.ai/free/en/authors/amirbek-djanibekov/), [Chiyu Zhang](https://md.research.elamur.ai/free/en/authors/chiyu-zhang/), [Hamad Alshehhi](https://md.research.elamur.ai/free/en/authors/hamad-alshehhi/), [Hanan Aldarmaki](https://md.research.elamur.ai/free/en/authors/hanan-aldarmaki/), [Mustafa Jarrar](https://md.research.elamur.ai/free/en/authors/mustafa-jarrar/), [Nizar Habash](https://md.research.elamur.ai/free/en/authors/nizar-habash/) and [Muhammad Abdul-Mageed](https://md.research.elamur.ai/free/en/authors/muhammad-abdul-mageed/) · **Year:** 2025 · **Venue:** [Proceedings of The Third Arabic Natural Language Processing Conference: Shared Tasks](https://md.research.elamur.ai/free/en/venues/arabicnlp/) ([ArabicNLP 2025](https://md.research.elamur.ai/free/en/venues/arabicnlp/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.arabicnlp-sharedtasks.99/) · [PDF](https://aclanthology.org/2025.arabicnlp-sharedtasks.99.pdf) · [DOI 10.18653/v1/2025.arabicnlp-sharedtasks.99](https://doi.org/10.18653/v1/2025.arabicnlp-sharedtasks.99) · [OpenAlex](https://openalex.org/W4416037633)

**Citations:** 1 citations (OpenAlex, 2026-10-14) · [14](https://md.research.elamur.ai/free/en/papers/talafha-2025-nadi-2025-first-multidialectal-arabic/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
NADI 2025 is the sixth edition of the Nuanced Arabic Dialect Identification series. Earlier editions worked on written text. This one moves to speech, because everyday Arabic is mostly spoken dialect, and few datasets annotate it. The shared task has three linked subtasks:
- eight-way country-level spoken dialect identification,
- multidialectal ASR over the same eight dialects,
- a new diacritic restoration task for spoken varieties, where systems may use the audio as well as the undiacritized text.

The organizers built new blind test sets and one common evaluation framework. Of 44 registered teams, eight submitted 100 valid runs. The best scores were 79.8% accuracy for dialect ID, 35.68 WER / 12.20 CER for ASR and 55 WER / 13 CER for diacritization.

Participants used fine-tuned Whisper and w2v-BERT-2.0 models, CTC decoding with n-gram language models, and multimodal LLMs. Even strong pretrained models leave a lot of headroom on dialect variation, code-switching and diacritics. The released data and leaderboards give future Arabic speech work a shared, comparable reference point.

## Key points
- **Dialects:** Algerian, Egyptian, Emirati, Jordanian, Mauritanian, Moroccan, Palestinian and Yemeni. The diacritization data also covers MSA, CA, mixed dialects and code-switched speech.
- **Blind test data:**
  - about eight hours of speech for dialect ID,
  - 10,807 utterances for ASR,
  - 1,332 utterances for diacritic restoration, which has separate open and closed tracks.
- **Participation:** 34 submissions from five teams (dialect ID), 47 from six teams (ASR) and 19 from two teams (diacritics). Seven system papers were accepted.
- **ASR systems:**
  - BYZÖ-CTC fine-tuned w2v-BERT-2.0 (580M parameters) and added a multidialectal 3-gram Kneser-Ney language model.
  - MarsadLab ran Whisper-Large zero-shot.
- **Diacritization systems:**
  - Unicorn fine-tuned GEMM3N with LoRA on audio plus text.
  - Abjad AI's CATT-Whisper fused a CATT text encoder with a Whisper encoder.

## Interesting ideas
- CATT-Whisper sometimes switches off the speech input during training, so the model still works when only text is available; this guards against an optional input going missing at test time.
- One ASR team adapted in two stages, first on pooled data from all dialects and then per dialect, so shared knowledge is learned before variety-specific tuning.
- The authors warn that a single reference transcription penalizes alternative outputs that are also correct, which makes WER/CER underestimate quality.
- Pairing a neural acoustic model with a cheap, explicit 3-gram language model at decoding time improved transcription at low cost.
- Separate open and closed tracks show whether gains come from the method itself or from extra resources.

## For Mova

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

- **I1** (tree): gparse could randomly drop auxiliary inputs such as tagger or lexicon features during training, so parsing stays stable when those signals are absent or wrong.
- **I2** (tree): train the parser on pooled treebanks first, then briefly fine-tune per genre or UD dialect, rather than training isolated models.
- **I3** (back): when scoring generation or the text → graph → text compressor, accept several valid surface forms, or compare at the meaning-graph level instead of by string match.
- **I4** (tokens): an explicit n-gram model over tag or lemma sequences could rerank the perceptron tagger's candidates and stay fully inspectable.
- **I5** (learn): report every evaluation twice, once with gold treebank data only and once with silver or LLM-teacher data, to attribute improvements honestly.

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

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- [Hamad bin Khalifa University](https://md.research.elamur.ai/free/en/universities/hamad-bin-khalifa-university/) — University, Doha, Qatar · 141 works · 14,175 citations
- [University of British Columbia](https://md.research.elamur.ai/free/en/universities/university-of-british-columbia/) — University, Vancouver, Canada · 98 works · 34,805 citations
- [New York University Abu Dhabi](https://md.research.elamur.ai/free/en/universities/new-york-university-abu-dhabi/) — University, Abu Dhabi, United Arab Emirates · 122 works · 4,602 citations
- [Mohamed bin Zayed University of Artificial Intelligence](https://md.research.elamur.ai/free/en/universities/mohamed-bin-zayed-university-of-artificial-intelligence/) — University, Abu Dhabi, United Arab Emirates · 124 works · 5,676 citations

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## Cite

Cite as: Bashar Talafha, Hawau Olamide Toyin, Peter Sullivan, AbdelRahim A. Elmadany, Abdurrahman Juma, Amirbek Djanibekov, Chiyu Zhang, Hamad Alshehhi, Hanan Aldarmaki, Mustafa Jarrar, Nizar Habash and Muhammad Abdul-Mageed. 2025. NADI 2025: The First Multidialectal Arabic Speech Processing Shared Task. Proceedings of The Third Arabic Natural Language Processing Conference: Shared Tasks.

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

```bibtex
@inproceedings{talafha2025nadi,
  title = {{NADI 2025: The First Multidialectal Arabic Speech Processing Shared Task}},
  author = {Bashar Talafha, Hawau Olamide Toyin, Peter Sullivan, AbdelRahim A. Elmadany, Abdurrahman Juma, Amirbek Djanibekov, Chiyu Zhang, Hamad Alshehhi, Hanan Aldarmaki, Mustafa Jarrar, Nizar Habash and Muhammad Abdul-Mageed},
  booktitle = {Proceedings of The Third Arabic Natural Language Processing Conference: Shared Tasks},
  year = {2025},
  doi = {10.18653/v1/2025.arabicnlp-sharedtasks.99},
  url = {https://doi.org/10.18653/v1/2025.arabicnlp-sharedtasks.99}
}
```

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- [↑ back to papers of 2025](https://md.research.elamur.ai/free/en/papers/2025/page/6/)

## References

91 references, 56 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 1 November 2026*