---
title: "Utilisation de la translittération arabe pour l'amélioration de"
description: "The authors build a transliterator that turns Arabic names, with or without vowel marks, into the range of plausible Latin forms. 17 references."
authors: ["Houda Saadane", "Nasredine Semmar"]
year: 2012
venue: "Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 2: TALN"
source: "https://aclanthology.org/F12-2010/"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/saadane-2012-utilisation-de-la-translitteration-arabe/"
---
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# Utilisation de la translittération arabe pour l'amélioration de l'alignement de mots à partir de corpus parallèles français-arabe (Using Arabic Transliteration to Improve Word Alignment from French-Arabic Parallel Corpora) \[in French\]

**Authors:** [Houda Saadane](https://md.research.elamur.ai/free/en/authors/houda-saadane/) and [Nasredine Semmar](https://md.research.elamur.ai/free/en/authors/nasredine-semmar/) · **Year:** 2012 · **Venue:** [Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 2: TALN](https://md.research.elamur.ai/free/en/venues/jeptalnrecital/) ([JEP-TALN-RECITAL (the French-speaking conferences on speech and natural language processing) 2012](https://md.research.elamur.ai/free/en/venues/jeptalnrecital/2012/)) · **Publisher:** [ATALA](https://md.research.elamur.ai/free/en/publishers/atala/) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/F12-2010/) · [PDF](https://aclanthology.org/F12-2010.pdf)

## Summary
The paper tackles a weakness in French–Arabic word alignment: proper names. Arabic names reach Latin script in many competing spellings, so a bilingual lexicon alone often misses them. The authors build a transliterator that turns Arabic names, with or without vowel marks, into the range of plausible Latin forms. They do not impose a single standard. Instead, they assembled the letter equivalences bottom-up from published norms and from spellings actually seen online, in a corpus covering the target languages.

This transliterator feeds the cognate-matching step of an existing linguistic aligner. That aligner pairs single words using a bilingual lexicon plus cues such as position, part of speech, named-entity status and cognates. It then pairs compound words through the syntactic dependency relations that connect their parts.

The system was evaluated in two ways: against a manually built reference alignment, and through Moses translation quality. Both evaluations improve, including a gain of +0.32 BLEU. This matters for building bilingual lexicons, for machine translation and for cross-language information retrieval, where name variation is a major source of misses.

## Key points
- Name variation is severe. Mouammar Kadhafi has more than 60 Latin spellings, and Lawrence of Arabia spelled Jeddah three different ways in a single 1926 book (25, 6 and 1 times).
- Standard Arabic script has 28 letters (25 consonants, 3 vowels). Short vowels and consonant doubling (shadda, tanwin) are usually left unwritten, which is the main source of ambiguity.
- The corpus study found that informal Latin-script Arabic (SMS, social sites) uses digits such as 3, 7, 9 and 6 for letters with no Latin equivalent.
- The authors attribute spelling variation partly to colonial history (French influence in the Maghreb, British/American influence in the Middle East) and partly to the absence of any common transliteration norm.
- Adding transliteration-based cognate alignments to the Moses training data raised BLEU by +0.32. The authors note this is hard to call significant, since training used only 10,000 sentence pairs for the translation model and about 11,000 sentences for the language model.

## Interesting ideas
- Treat a name as a set of plausible spellings generated by a one-to-many mapping, and accept a match when any variant fits, instead of forcing one canonical form.
- Derive character correspondences empirically from how people actually write, including informal conventions like digits for missing letters, rather than from an official norm.
- Align in two stages: first anchor single words with a lexicon and cheap cues (position, POS, entity type, cognates), then align multiword units by following the dependency relations between their components.
- Evaluate a component both intrinsically against a hand-made reference and extrinsically through a downstream task, and openly flag when a small downstream gain may not be significant.

## For Mova

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

- **I1** (world): Wikidata-derived entities in the knowledge graph could carry generated spelling-variant sets, so mentions like "Gadhafi" and "Qaddafi" resolve to the same entity.
- **I2** (tokens): a normalization layer for noisy English input (leetspeak, phonetic or nonstandard spellings) could be learned from observed usage before lemmatization.
- **I3** (back): translation through the meta-world could match multiword expressions as UD subtrees anchored on already-aligned head words, which keeps every alignment explainable.
- **I4** (learn): self-induced repair rules could be scored on treebank accuracy and on answer-task accuracy, with small gains on small test sets treated as unproven.

*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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## Cite

Cite as: Houda Saadane and Nasredine Semmar. 2012. Utilisation de la translittération arabe pour l'amélioration de l'alignement de mots à partir de corpus parallèles français-arabe (Using Arabic Transliteration to Improve Word Alignment from French-Arabic Parallel Corpora) \[in French\]. Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 2: TALN.

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

```bibtex
@inproceedings{saadane2012utilisation,
  title = {{Utilisation de la translittération arabe pour l'amélioration de l'alignement de mots à partir de corpus parallèles français-arabe (Using Arabic Transliteration to Improve Word Alignment from French-Arabic Parallel Corpora) [in French]}},
  author = {Houda Saadane and Nasredine Semmar},
  booktitle = {Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 2: TALN},
  year = {2012},
  url = {https://aclanthology.org/F12-2010/}
}
```

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## References

17 references, 2 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 24 October 2026*