---
title: "Dialectal to Standard Arabic Paraphrasing to Improve"
description: "Arabic-English statistical MT performs poorly on dialectal Arabic (DA). 42 references, cited by 50 papers here."
authors: ["Wael Salloum", "Nizar Habash"]
year: 2011
venue: "Proceedings of the First Workshop on Algorithms and Resources for Modelling of Dialects and Language Varieties"
source: "https://aclanthology.org/W11-2602/"
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/salloum-2011-dialectal-standard-arabic-paraphrasing-improve/"
---
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# Dialectal to Standard Arabic Paraphrasing to Improve Arabic-English Statistical Machine Translation

**Authors:** [Wael Salloum](https://md.research.elamur.ai/free/en/authors/wael-salloum/) and [Nizar Habash](https://md.research.elamur.ai/free/en/authors/nizar-habash/) · **Year:** 2011 · **Venue:** Proceedings of the First Workshop on Algorithms and Resources for Modelling of Dialects and Language Varieties · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **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/W11-2602/) · [PDF](https://aclanthology.org/W11-2602.pdf) · [OpenAlex](https://openalex.org/W1544567521)

**Citations:** 77 citations (OpenAlex, 2026-10-14) · [50](https://md.research.elamur.ai/free/en/papers/salloum-2011-dialectal-standard-arabic-paraphrasing-improve/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Arabic-English statistical MT performs poorly on dialectal Arabic (DA). Dialects lack standard spelling and parallel data, and they use clitics and affixes that Modern Standard Arabic (MSA) tools do not recognize. Salloum and Habash avoid building a full dialect translator. Instead they rewrite troublesome dialectal words into MSA forms the MT system already knows. Their analyzer, ADAM, extends the existing MSA analyzer (ALMOR, built on the BAMA databases) with fewer than 40 Levantine and Egyptian clitics. A set of 11 morphological transfer rules then maps the analyses to MSA. The candidate paraphrases are passed to a phrase-based SMT system as a lattice rather than as a single normalized sentence. On a blind test set, BLEU rises by 0.56 absolute (1.5% relative). Manual inspection finds correct translations for 74% of handled OOVs and 60% of dialectal low-frequency words. The work shows that a small, cheap layer of morphological rules can partly bridge a resource-rich standard language and its under-resourced varieties.

## Key points
- On the dev set, 1.51% of tokens and 7.45% of types are OOV, yet nearly a third of sentences (31.48%) contain at least one OOV. MSA test data such as NIST MTEval 2006 has a token OOV rate of only 0.8%.
- 74.1% of OOVs are dialectal. Words combining an MSA-like stem with dialectal affixes or clitics make up 49.7% of all OOVs, and this is the only class the paper targets. Lexical (stem) translation is left for future work.
- ALMOR alone can analyze only 21% of the OOV words. ADAM adds under 40 dialectal clitics for Levantine and Egyptian, and 11 transfer rules produce MSA paraphrases.
- In a manual sample of 68 OOV instances, 22% were left unhandled, 74% of the handled cases were translated correctly, and no OOVs were deleted. Most errors came from misspellings, lexical ambiguity and proper names.
- Extending the method to low-frequency (LF) words raised accuracy on dialectal LF words from 42% to 60%. However, accuracy on MSA LF words fell from 64% to 55%.

## Interesting ideas
- The system hands the MT decoder a lattice of several alternative normalizations and lets the downstream model choose, instead of committing early to one "standard" rewrite.
- Rather than building a new dialect analyzer, the authors add a small inventory of nonstandard clitics on top of a mature standard analyzer, reusing its stems and machinery.
- Before writing any rules, the authors classify the OOVs and pick the largest class that needs no new lexicon, which gives the best gain per unit of effort.
- The evaluation separates "not handled", "mistranslated" and "corrected" outcomes. It also checks whether the fix damages standard input, and finds that LF handling hurt MSA words.
- The authors translate rare or unseen forms indirectly by paraphrasing them into in-vocabulary equivalents, using the standard variety as a bridge.

## For Mova

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

- **I1** (tokens): For informal or nonstandard English tokens, the tokenizer could output several candidate analyses with their provenance, and the tagger and parser would make the final choice.
- **I2** (tokens): The crate en morphology could gain a small extension table for colloquial contractions and clitics (e.g. "gonna", "y'all", "'em") that maps them to standard lemmas plus features. The original surface form would be kept for explainability.
- **I3** (learn): Self-induced repair rules could be prioritized by first bucketing parse or lemma failures by cause and then attacking the biggest cheap bucket.
- **I4** (learn): Every normalization or repair rule should be evaluated with a negative control on clean, standard text, so that gains on noisy input do not quietly cost accuracy elsewhere.
- **I5** (frames): A rare or colloquial verb could be paraphrased to a frequent standard verb to retrieve its VerbNet class and roles. The paraphrase link would be recorded so the frame stays explainable.

*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: Wael Salloum and Nizar Habash. 2011. Dialectal to Standard Arabic Paraphrasing to Improve Arabic-English Statistical Machine Translation. Proceedings of the First Workshop on Algorithms and Resources for Modelling of Dialects and Language Varieties.

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

```bibtex
@inproceedings{salloum2011dialectal,
  title = {{Dialectal to Standard Arabic Paraphrasing to Improve Arabic-English Statistical Machine Translation}},
  author = {Wael Salloum and Nizar Habash},
  booktitle = {Proceedings of the First Workshop on Algorithms and Resources for Modelling of Dialects and Language Varieties},
  year = {2011},
  doi = {10.7916/d8697bg2},
  url = {https://doi.org/10.7916/d8697bg2}
}
```

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## References

42 references, 16 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*