---
title: "Domain Adaptation for Arabic Machine — Alghamdi 2023"
description: "The paper looks at how well machine translation from Arabic to English holds up in finance, a specialised domain that Arabic MT research had not yet…"
authors: ["Emad A. Alghamdi", "Jezia Zakraoui", "Fares A. Abanmy"]
year: 2023
venue: "arXiv:2309.12863"
source: "https://arxiv.org/abs/2309.12863"
doi: "10.48550/arxiv.2309.12863"
arxiv: "2309.12863"
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/alghamdi-2023-domain-adaptation-arabic-machine-translation/"
---
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# Domain Adaptation for Arabic Machine Translation: The Case of Financial Texts

**Authors:** [Emad A. Alghamdi](https://md.research.elamur.ai/free/en/authors/emad-a-alghamdi/), Jezia Zakraoui, Fares A. Abanmy · **Year:** 2023 · **Venue:** arXiv:2309.12863 · **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/2309.12863) · [PDF](https://arxiv.org/pdf/2309.12863) · [DOI 10.48550/arxiv.2309.12863](https://doi.org/10.48550/arxiv.2309.12863) · [arXiv 2309.12863](https://arxiv.org/abs/2309.12863) · [OpenAlex](https://openalex.org/W4387031599)

**Citations:** 1 citations (openalex-snapshot · 2026-10-14) · 2 in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper looks at how well machine translation from Arabic to English holds up in finance, a specialised domain that Arabic MT research had not yet covered. Earlier Arabic domain-adaptation work dealt mainly with news and medical text. The authors built what they describe as the first AR-EN parallel corpus of financial news, carefully aligned at the segment level, and used it as a benchmark. On this corpus they fine-tuned two pre-trained NMT models, Helsinki-NLP opus-mt-ar-en and NLLB-200-distilled-600M, and also fine-tuned ChatGPT-3.5 Turbo. They compared training on authentic pairs alone with training on data enlarged by machine-translated and synthetic pairs. Evaluation combined automatic metrics (lexical ones plus COMET and BERTScore) with ratings from human experts. Three findings stand out:
- A small set of clean in-domain pairs is enough for fine-tuning to work.
- ChatGPT-3.5 gave the best translations overall.
- Adding augmented data generally made quality worse.

The work matters because it supplies public data and fine-tuned models for a domain and language pair with few resources, and because it is presented as the first study of adapting ChatGPT to the financial domain.

## Key points
- The resources are a new financial-news AR-EN parallel corpus and several fine-tuned checkpoints, published under the asas-ai organisation on Hugging Face. Examples include opus-mt-ar-en-finetuned-ar-to-en and nllb-200-distilled-600M variants trained on authentic, MT-augmented and synthetic-augmented data.
- The fine-tuned models are OPUS-MT (ar-en), NLLB-200-distilled-600M and ChatGPT-3.5 Turbo. They were compared across three training setups.
- When ChatGPT-3.5 was trained on augmented data, its COMET score fell to 23.03 while its BERTScore stayed high at 0.91. The authors read this as a disagreement between the two metrics.
- Human evaluation worked as follows:
  - Three native-speaker finance postgraduates rated 50 random test sentences, seeing both source and translation.
  - They used a 1–4 acceptability scale running from Unacceptable to Ideal.
  - ChatGPT-3.5 received the top mean rating of 3.1 in setups #1 and #2.
  - In setup #3, the models were rated about equally.
- BERTScore matched the human judgments more closely than COMET did. Of the NMT models, OPUS coped better with back-translated augmentation than with synthetic augmentation.

## Interesting ideas
- A small, well-aligned in-domain set was enough for successful adaptation, while adding synthetic or machine-translated pairs tended to hurt, because their errors carry over into the model's outputs. — For Mova (learn): Prefer a small set of silver examples on which all witness parsers agree over a large set where they disagree, and test whether adding the lower-agreement data actually lowers parser accuracy.
- Augmentation sources were kept apart (back-translated versus synthetic), and a separate model was trained and released for each, so the effect of each source could be measured on its own. — For Mova (learn): Label each silver or LLM-teacher batch by where it came from and train ablations per source, so a harmful teacher tier can be identified and removed.
- When two automatic metrics disagreed (COMET versus BERTScore), the authors used human ratings to decide which metric to trust. — For Mova (back): Before choosing a single score for the text → graph → text compressor's output, check each automatic score against a small human-rated sample.
- Human raters saw the source alongside the output, scored on a four-level acceptability scale, and had to explain any extreme rating. This yields judgments that come with reasons. — For Mova (back): Use the same 1–4 rubric, with required justifications for extreme scores, when evaluating generated text and translations through the meta-world. The written reasons can then be mined for repair rules.

*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{alghamdi2023domain,
  title = {{Domain Adaptation for Arabic Machine Translation: The Case of Financial Texts}},
  author = {Alghamdi, Emad A. and Zakraoui, Jezia and Abanmy, Fares A.},
  year = {2023},
  doi = {10.48550/arxiv.2309.12863},
  eprint = {2309.12863},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2309.12863}
}
```

- [← previous DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models](https://md.research.elamur.ai/free/en/papers/chuang-2023-dola-decoding-contrasting-layers-improves/)
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