---
title: "Arabic and computer-aided translation: an integrated approach"
description: "The author proposes a set of workarounds: macros that insert punctuation, an Arabic spelling corrector, undiacritised… 91 references."
authors: ["Barbara Quaranta"]
year: 2011
venue: "Proceedings of Translating and the Computer 33"
source: "https://aclanthology.org/2011.tc-1.14/"
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/quaranta-2011-arabic-computer-aided-translation-integrated/"
---
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# Arabic and computer-aided translation: an integrated approach

**Authors:** Barbara Quaranta · **Year:** 2011 · **Venue:** [Proceedings of Translating and the Computer 33](https://md.research.elamur.ai/free/en/venues/tc/) ([Translating and the Computer 2011](https://md.research.elamur.ai/free/en/venues/tc/2011/)) · **Publisher:** [Aslib](https://md.research.elamur.ai/free/en/publishers/aslib/) · **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/2011.tc-1.14/) · [PDF](https://aclanthology.org/2011.tc-1.14.pdf)

## Summary
This paper asks why translators of Arabic rarely use computer-aided translation (CAT) tools, and what goes wrong when they do. These tools were designed around Western languages. The author ran an experiment with SDL Trados 2007, translating two Arabic preliminary sales contracts into English. One was a blank template and the other a real signed agreement. A short survey of Arabic translators guided which texts to choose.

Two families of failure appeared:
- **Formatting:** fonts, sparse punctuation, and symbols used in place of punctuation broke segmentation.
- **Morphology:** attached prefixes, suffixes and infixes, plus optional diacritics, stopped translation-memory and termbase lookups from matching.

The author proposes a set of workarounds:
- macros that insert punctuation,
- an Arabic spelling corrector,
- undiacritised memories,
- tuned fuzzy-match thresholds,
- a custom tool that expands each term with every possible prefix before termbase import.

The paper then reviews Arabic computational linguistics. It argues that morphological analysis, already used in machine translation, should be built into CAT tools. This would make the software properly internationalised rather than patched from outside.

## Key points
- **Tool and test data:** SDL Trados 2007 was tested on two Arabic preliminary sales contracts translated into English. Segmentation, translation memories, termbases and fuzzy matching were all examined.
- **Segmentation fix:** Sentences often lack punctuation, so default segmentation rules fail. Substitution macros replaced recurring expressions and symbols that act like punctuation with real punctuation marks. Orthographic variants were handled with the Microsoft Office Multilingual User Interface Pack.
- **Diacritics:** The software treats diacritics as ordinary characters, so vocalised and plain forms of a word do not match. The suggested mitigations are:
  - store memories and termbases without diacritics,
  - add vocalised variants through Multiple translation options,
  - lower the fuzzy-match threshold.
- **Prefix expansion tool:** An Access application (Visual Basic/SQL) produces an Excel sheet listing every prefixed form of a term. MultiTerm Convert turns it into XML for a MultiTerm termbase. The tool was built with research at Cairo University and a morphological analysis by an R.D.I. researcher.
- **Remaining limitation:** Suffixes, such as regular plurals, are only partly caught by lowering fuzzy match. Short words with long suffixes still fail because matching is percentage-based. The paper cites Farghaly (2010), Sawaf (2010) and Tymoczko (1998).

## Interesting ideas
- Before splitting sentences, rewrite recurring phrases and symbols that act as boundaries into explicit punctuation, so a generic segmenter works on poorly punctuated text.
- Instead of making the matcher smarter, enlarge the lexicon by generating every affixed surface form of a term ahead of time, so plain exact lookup succeeds.
- Optional marks such as diacritics should not count as identity-changing characters; keep one normalised key and attach the marked variants to it.
- Character-percentage similarity breaks down when affixes are long relative to the stem, so similarity should be measured on morphological units, not raw strings.
- Ask practitioners which documents cause them trouble before picking test material, so the evaluation targets real failure cases.

## For Mova

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

- **I1** (tokens): A pre-segmentation normaliser could map list markers, dashes or connective formulas in legal or informal English to sentence breaks, and log each rewrite so the change stays explainable.
- **I2** (world): Ontology and knowledge-graph entries from WordNet or Wikidata could be pre-expanded with inflected and possessive forms, keeping each variant linked to its lemma for traceable entity matching.
- **I3** (tokens): The tokenizer could hold a stripped key for accents, curly quotes and ligatures alongside the original string, so lookups succeed while the surface text is preserved.
- **I4** (learn): Evaluation of matching and repair rules could report results by word length or affix ratio, as a control that exposes string-similarity shortcuts.
- **I5** (answer): Test sets for the readers could be seeded from teachers' reports of question types students stumble on, rather than only from convenient corpora.

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

📚 Shelves (2): [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 9,296 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 9,206 works

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## Cite

Cite as: Barbara Quaranta. 2011. Arabic and computer-aided translation: an integrated approach. Proceedings of Translating and the Computer 33.

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

```bibtex
@inproceedings{quaranta2011arabic,
  title = {{Arabic and computer-aided translation: an integrated approach}},
  author = {Barbara Quaranta},
  booktitle = {Proceedings of Translating and the Computer 33},
  year = {2011},
  url = {https://aclanthology.org/2011.tc-1.14/}
}
```

- [← previous A.P. Cowie (Editor). The Oxford History of English Lexicography. Volume I: General-purpose Dictionaries. Volume II: Specialized Dictionaries](https://md.research.elamur.ai/free/en/papers/palfi-2011-p-cowie-editor-oxford-history/)
- [next → Automatic transcription of 17th century English text in Contemporary English with NooJ: Method and Evaluation](https://md.research.elamur.ai/free/en/papers/piton-2011-automatic-transcription-17th-century-english/)
- [↑ back to papers of 2011](https://md.research.elamur.ai/free/en/papers/2011/page/2/)

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*Updated 24 October 2026*