---
title: "DarijaBanking — Skiredj 2024"
description: "Moroccan Darija mixes Arabic with French, Spanish and Tamazight influences and differs substantially from Modern Standard Arabic."
authors: ["Abderrahman Skiredj", "Ferdaous Azhari", "Ismail Berrada", "Saad Ezzini"]
year: 2024
venue: "arXiv:2405.16482"
source: "https://arxiv.org/abs/2405.16482"
doi: "10.48550/arxiv.2405.16482"
arxiv: "2405.16482"
licence: "CC BY-NC-SA 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by-nc-sa/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/skiredj-2024-darijabanking-new-resource-overcoming-language/"
---
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# DarijaBanking: A New Resource for Overcoming Language Barriers in Banking Intent Detection for Moroccan Arabic Speakers

**Authors:** [Abderrahman Skiredj](https://md.research.elamur.ai/free/en/authors/abderrahman-skiredj/), Ferdaous Azhari, [Ismail Berrada](https://md.research.elamur.ai/free/en/authors/ismail-berrada/), [Saad Ezzini](https://md.research.elamur.ai/free/en/authors/saad-ezzini/) · **Year:** 2024 · **Venue:** arXiv:2405.16482 · **Licence:** [CC BY-NC-SA 4.0 (arXiv)](http://creativecommons.org/licenses/by-nc-sa/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2405.16482) · [PDF](https://arxiv.org/pdf/2405.16482) · [DOI 10.48550/arxiv.2405.16482](https://doi.org/10.48550/arxiv.2405.16482) · [arXiv 2405.16482](https://arxiv.org/abs/2405.16482) · [OpenAlex](https://openalex.org/W4405083349)

**Citations:** 3 citations (openalex-snapshot · 2026-10-14) · 2 in our library, of the 140884 papers whose bibliographies we read

## Summary
Moroccan Darija mixes Arabic with French, Spanish and Tamazight influences and differs substantially from Modern Standard Arabic. Banking chatbots for Moroccan customers therefore need dedicated data to recognize what a user is asking for. The authors build DarijaBanking, a parallel intent-detection corpus in Darija, MSA, English and French. They start from three English banking collections, roughly 42,000 questions in total. They merge the intent schemes, drop redundant or locally irrelevant items and keep 1,800 English queries. Machine translation then produces French, MSA and Darija versions, with GPT-4 handling Darija. Five native speakers checked every Darija item. On this data the authors compare several approaches:
- fine-tuning monolingual and multilingual encoders,
- zero-shot transfer,
- embedding retrieval,
- LLM prompting,
- a translate-to-English pipeline.

Their own BERT-based classifier, BERTouch, reaches F1 of about 0.98 on Darija and 0.96 on MSA, which beats GPT-4. The study shows that a small specialized classifier trained on curated in-domain dialect data outperforms general LLMs. Retrieval is a cheaper fallback when labeling budgets are tight.

## Key points
- DarijaBanking has 1,800 queries per language across 4 languages (7,200 total), labeled with 24 intents.
- The source datasets were Banking77 (13,083 queries, 77 intents), Banking-Faq-Bot (1,764 queries, 7 intents) and Smart-Banking-Chatbot (30,100 queries, 41 intents).
- French was translated with OPUS MT, MSA with Turjuman and Darija with GPT-4. Five native annotators reviewed all utterances and edited about 47% of them.
- BERTouch, released on Hugging Face, scored F1 0.98 on Darija and 0.96 on MSA, outperforming ChatGPT-3.5 and GPT-4 prompting.
- A pipeline of hf-seamless-m4t-large translation into English plus a Bert-base-uncased classifier reached F1 83.70 on MSA and 89.03 on Darija, with a corpus BLEU of 0.328 against the original English. For a cheaper option, retrieval with text-embedding-3-large or multilingual-e5-base performed reasonably.

## Interesting ideas
- The authors let an LLM draft translations and then had native speakers review every item, reporting the share that needed edits (~47%). That share serves as a direct measure of how trustworthy the machine draft was. — For Mova (learn): Track the human or witness correction rate per LLM teacher tier and per construction type. This would show which tiers can produce silver data unchecked and which always need review.
- Before translating, they merged label sets from several source datasets into one schema and removed items that did not fit the target setting. This produced a small, coherent label space instead of a large, noisy union. — For Mova (world): When combining ontologies from WordNet, FrameNet and Wikidata, do an explicit unification and pruning pass first. Overlapping or irrelevant categories then won't fragment the compiled knowledge.
- They used a translate-into-a-pivot-language-then-classify pipeline as a baseline and measured translation quality with BLEU. This separates translation errors from classification errors. — For Mova (back): When evaluating translation through the meta-world, score the pivot step and the downstream task separately. That locates the stage where meaning is lost.
- They kept cheap baselines (nearest-example retrieval over embeddings, LLM prompting) next to the specialized model. This makes the value of curated in-domain data measurable rather than assumed. — For Mova (learn): Add embedding-retrieval and plain-LLM answers as standing negative controls in evaluation. The explainable tree-and-frames path must then beat these shallow shortcuts to justify itself.

*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](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/)

## Cite

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

```bibtex
@misc{skiredj2024darijabanking,
  title = {{DarijaBanking: A New Resource for Overcoming Language Barriers in Banking Intent Detection for Moroccan Arabic Speakers}},
  author = {Skiredj, Abderrahman and Azhari, Ferdaous and Berrada, Ismail and Ezzini, Saad},
  year = {2024},
  doi = {10.48550/arxiv.2405.16482},
  eprint = {2405.16482},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2405.16482}
}
```

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