---
title: "Morphological Analysis and Disambiguation for Dialectal Arabic"
description: "Most Arabic NLP tools are built for Modern Standard Arabic (MSA) and handle dialects poorly… 41 references, cited by 68 papers here."
authors: ["Nizar Habash", "Ryan Roth", "Owen Rambow", "Ramy Eskander", "Nadi Tomeh"]
year: 2013
venue: "Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies"
source: "https://aclanthology.org/N13-1044/"
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/habash-2013-morphological-analysis-disambiguation-dialectal-arabic/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/habash-2013-morphological-analysis-disambiguation-dialectal-arabic/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2013](https://md.research.elamur.ai/free/en/papers/2013/) / Morphological Analysis and Disambiguation for Dialectal Arabic

# Morphological Analysis and Disambiguation for Dialectal Arabic

**Authors:** [Nizar Habash](https://md.research.elamur.ai/free/en/authors/nizar-habash/), [Ryan Roth](https://md.research.elamur.ai/free/en/authors/ryan-roth/), [Owen Rambow](https://md.research.elamur.ai/free/en/authors/owen-rambow/), [Ramy Eskander](https://md.research.elamur.ai/free/en/authors/ramy-eskander/) and [Nadi Tomeh](https://md.research.elamur.ai/free/en/authors/nadi-tomeh/) · **Year:** 2013 · **Venue:** [Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies](https://md.research.elamur.ai/free/en/venues/naacl/) ([NAACL 2013](https://md.research.elamur.ai/free/en/venues/naacl/2013/)) · **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/N13-1044/) · [PDF](https://aclanthology.org/N13-1044.pdf) · [OpenAlex](https://openalex.org/W2250732891)

**Citations:** 124 citations (OpenAlex, 2026-10-14) · [68](https://md.research.elamur.ai/free/en/papers/habash-2013-morphological-analysis-disambiguation-dialectal-arabic/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Most Arabic NLP tools are built for Modern Standard Arabic (MSA) and handle dialects poorly, because dialects differ in vocabulary, morphology, and spelling and have no standard orthography. The authors adapt MADA, a state-of-the-art MSA tagger that resolves all morphological features of a word in context at once, to Egyptian Arabic (ARZ). The result is MADA-ARZ. In place of SAMA, the analyzer designed for MSA, they use CALIMA, an analyzer built for Egyptian Arabic that also normalizes spellings to CODA. They retrain the feature classifiers on LDC Egyptian Arabic Treebank data after first aligning its annotations with CALIMA. They also drop MSA-only features such as case and mood and add dialect-specific values. On the ATB-ARZ test set, MADA-ARZ outperforms the MSA version on every metric, for example 75.4% vs. 27.0% on the full-analysis metric. It also beats a prior Egyptian segmenter and improves Egyptian-to-English phrase-based MT. The work shows that a feature-rich disambiguation framework can move to a new language variety by swapping in a matching analyzer and annotated corpus.

## Key points
- **Training data:** ATB-ARZ (Egyptian Arabic Treebank, parts 1–5) provides 134K training words, split roughly 10/80/10 into dev/train/test. ATB-123 (MSA) provides 711K words and was used both for the baseline and for a combined "ALL" setting.
- **Annotation alignment:** After the LDC annotations were synchronized with CALIMA, 90% of words matched the original full tag. In a manual check of 100 mismatches, the synchronized choice was correct or acceptable in 55% of cases, giving an estimated accuracy above 95% overall.
- **Test results, MADA (MSA) vs. MADA-ARZ (ARZ-only training):**
  - Morph Tag: 35.7 vs. 84.5
  - Diacritic: 32.2 vs. 83.2
  - Lemma: 67.1 vs. 86.3
  - ATB Segmentation: 90.5 vs. 97.4
- **Effect of adding MSA data:** It helped only slightly, on Penn POS, MADA POS and segmentation. It hurt diacritics, lemmas and the full tag.
- **External comparison and MT:** On the CMUQ-ECA set, morph segmentation reached 93.2%, compared with 91.9% reported by Mohamed et al. In Moses MT, BLEU was 25.2 for MADA-ARZ, 24.4 for MADA and 22.1 for punctuation-only tokenization. The OOV rate fell from 5.8% to 4.9%.

## Interesting ideas
- **Separate the analyzer from the disambiguator.** A lexicon-based analyzer proposes candidate readings, and classifiers only rank them. Moving to a new variety then mostly means replacing the analyzer and retraining. This design could suit Ukrainian dialects or surzhyk in the same way.
- **Reconcile annotations with the analyzer before training.** Mapping the gold corpus onto the analyzer's representation, then manually auditing a sample of mismatches, gives a credible estimate of data quality. This is worth doing whenever an annotated corpus and a lexical resource were developed separately.
- **Normalize spelling inside the pipeline.** Mapping varied spellings to a conventional orthography (CODA) while accepting many input variants shrinks the vocabulary and reduces OOV words for downstream MT. It suits any language without a fixed spelling standard.
- **Test whether related-language data actually helps.** Mixing in a much larger standard-language corpus improved coarse tags but hurt fine-grained ones. Measuring this per metric is better than assuming more data is always helpful.
- **Evaluate both intrinsically and extrinsically.** The authors pair tagging metrics of increasing strictness with an MT experiment that also reports OOV rates. This shows whether tagging gains carry over to a real application.

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

## Institutions

- [Columbia University](https://md.research.elamur.ai/free/en/universities/columbia-university/) — University, New York, United States · 149 works · 48,852 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 9,086 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 8,889 works · [🎭 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

🧭 Topics (2): [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · 1,484 works · [🧩 Morphology](https://md.research.elamur.ai/free/en/topics/morphology/) · 277 works

## Cite

Cite as: Nizar Habash, Ryan Roth, Owen Rambow, Ramy Eskander and Nadi Tomeh. 2013. Morphological Analysis and Disambiguation for Dialectal Arabic. Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.

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

```bibtex
@inproceedings{habash2013morphological,
  title = {{Morphological Analysis and Disambiguation for Dialectal Arabic}},
  author = {Nizar Habash, Ryan Roth, Owen Rambow, Ramy Eskander and Nadi Tomeh},
  booktitle = {Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  year = {2013},
  url = {https://aclanthology.org/N13-1044/}
}
```

- [← previous Statistical Metaphor Processing](https://md.research.elamur.ai/free/en/papers/shutova-2013-statistical-metaphor-processing/)
- [next → A Dataset of Syntactic-Ngrams over Time from a Very Large Corpus of English Books](https://md.research.elamur.ai/free/en/papers/goldberg-2013-dataset-syntactic-ngrams-over-time/)
- [↑ back to papers of 2013](https://md.research.elamur.ai/free/en/papers/2013/)

## References

41 references, 15 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Ernest T. Abdel-Massih, Zaki N. Abdel-Malek, and El-Said M. Badawi. 1979. A Reference Grammar of Egyptian Arabic. Georgetown University Press. [find](https://scholar.google.com/scholar?q=Ernest+T%2E+Abdel%2DMassih%2C+Zaki+N%2E+Abdel%2DMalek%2C+and+El%2DSaid+M%2E+Badawi%2E+1979%2E+A+Reference+Grammar+of+Egyptian+Arabic%2E+Georgetown+University+Press%2E)
2. Hitham Abo Bakr, Khaled Shaalan, and Ibrahim Ziedan. 2008. A Hybrid Approach for Converting Written Egyptian Colloquial Dialect into Diacritized Arabic. In The 6th International Conference on Informatics and Systems, INFOS2008. Cairo University. [find](https://scholar.google.com/scholar?q=Hitham+Abo+Bakr%2C+Khaled+Shaalan%2C+and+Ibrahim+Ziedan%2E+2008%2E+A+Hybrid+Approach+for+Converting+Written+Egyptian+Colloquial+Dialect+into+Diacritized+Arabic%2E+In+The+6th+International+Conference+on+Informat)
3. Rania Al-Sabbagh and Roxana Girju. 2012. A supervised POS tagger for written Arabic social networking corpora. In Jeremy Jancsary, editor, Proceedings of KONVENS 2012, pages 39–52. ÖGAI, September. Main track: oral presentations. [find](https://scholar.google.com/scholar?q=Rania+Al%2DSabbagh+and+Roxana+Girju%2E+2012%2E+A+supervised+POS+tagger+for+written+Arabic+social+networking+corpora%2E+In+Jeremy+Jancsary%2C+editor%2C+Proceedings+of+KONVENS+2012%2C+pages+39%E2%80%9352%2E+%C3%96GAI%2C+September%2E+Ma)
4. Satanjeev Banerjee and Alon Lavie. 2005. [METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments](https://md.research.elamur.ai/free/en/papers/banerjee-2005-meteor-automatic-metric-mt-evaluation/). In Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization, pages 65–72, Ann Arbor, Michigan.
5. Eric Brill. 1995. [Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part-of-Speech Tagging](https://md.research.elamur.ai/free/en/papers/brill-1995-transformation-based-error-driven-learning/). Computational Linguistics, 21(4):543–565.
6. Tim Buckwalter. 2007. Issues in Arabic Morphological Analysis. In A. van den Bosch and A. Soudi, editors, Arabic Computational Morphology: Knowledge-based and Empirical Methods. Springer. [↗](https://doi.org/10.1007/978-1-4020-6046-5_3)
7. David Chiang, Mona Diab, Nizar Habash, Owen Rambow, and Safiullah Shareef. 2006. [Parsing Arabic Dialects](https://md.research.elamur.ai/free/en/papers/chiang-2006-parsing-arabic-dialects/). In Proceedings of the European Chapter of ACL (EACL). [↗](https://doi.org/10.7916/d85t3tzg)
8. Walter Daelemans and Antal van den Bosch. 2005. Memory-Based Language Processing. Studies in Natural Language Processing. Cambridge University Press, Cambridge, UK. [↗](https://doi.org/10.1017/cbo9780511486579)
9. Mona Diab, Kadri Hacioglu, and Daniel Jurafsky. 2007. Automated methods for processing arabic text: From tokenization to base phrase chunking. In Antal van den Bosch and Abdelhadi Soudi, editors, Arabic Computational Morphology: Knowledge-based and Empirical Methods. Kluwer/Springer. [find](https://scholar.google.com/scholar?q=Mona+Diab%2C+Kadri+Hacioglu%2C+and+Daniel+Jurafsky%2E+2007%2E+Automated+methods+for+processing+arabic+text%3A+From+tokenization+to+base+phrase+chunking%2E+In+Antal+van+den+Bosch+and+Abdelhadi+Soudi%2C+editors%2C+Arab)
10. Mona Diab. 2007. Improved Arabic Base Phrase Chunking with a New Enriched POS Tag Set. In Proceedings of the 2007 Workshop on Computational Approaches to Semitic Languages: Common Issues and Resources, pages 89–96, Prague, Czech Republic, June. [find](https://scholar.google.com/scholar?q=Mona+Diab%2E+2007%2E+Improved+Arabic+Base+Phrase+Chunking+with+a+New+Enriched+POS+Tag+Set%2E+In+Proceedings+of+the+2007+Workshop+on+Computational+Approaches+to+Semitic+Languages%3A+Common+Issues+and+Resources)
11. Kevin Duh and Katrin Kirchhoff. 2005. POS tagging of dialectal Arabic: a minimally supervised approach. In Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages, Semitic ’05, pages 55–62, Ann Arbor, Michigan. [find](https://scholar.google.com/scholar?q=Kevin+Duh+and+Katrin+Kirchhoff%2E+2005%2E+POS+tagging+of+dialectal+Arabic%3A+a+minimally+supervised+approach%2E+In+Proceedings+of+the+ACL+Workshop+on+Computational+Approaches+to+Semitic+Languages%2C+Semitic+%E2%80%9905)
12. Ramy Eskander, Nizar Habash, Owen Rambow, and Nadi Tomeh. 2013. Processing Spontaneous Orthography. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), Atlanta, GA. [find](https://scholar.google.com/scholar?q=Ramy+Eskander%2C+Nizar+Habash%2C+Owen+Rambow%2C+and+Nadi+Tomeh%2E+2013%2E+Processing+Spontaneous+Orthography%2E+In+Proceedings+of+the+2013+Conference+of+the+North+American+Chapter+of+the+Association+for+Computati)
13. Qin Gao and Stephan Vogel. 2008. [Parallel implementations of word alignment tool](https://md.research.elamur.ai/free/en/papers/gao-2008-parallel-implementations-word-alignment-tool/). In Software Engineering, Testing, and Quality Assurance for Natural Language Processing, SETQA-NLP ’08, pages 49–57, Stroudsburg, PA, USA. Association for Computational Linguistics.
14. David Graff, Mohamed Maamouri, Basma Bouziri, Sondos Krouna, Seth Kulick, and Tim Buckwalter. 2009. Standard Arabic Morphological Analyzer (SAMA) Version 3.1. Linguistic Data Consortium LDC2009E73. [find](https://scholar.google.com/scholar?q=David+Graff%2C+Mohamed+Maamouri%2C+Basma+Bouziri%2C+Sondos+Krouna%2C+Seth+Kulick%2C+and+Tim+Buckwalter%2E+2009%2E+Standard+Arabic+Morphological+Analyzer+%28SAMA%29+Version+3%2E1%2E+Linguistic+Data+Consortium+LDC2009E73%2E)
15. Nizar Habash and Owen Rambow. 2005. [Arabic Tokenization, Part-of-Speech Tagging and Morphological Disambiguation in One Fell Swoop](https://md.research.elamur.ai/free/en/papers/habash-2005-arabic-tokenization-part-speech-tagging/). In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05), pages 573–580, Ann Arbor, Michigan.
16. Nizar Habash and Owen Rambow. 2006. [MAGEAD: A Morphological Analyzer and Generator for the Arabic Dialects](https://md.research.elamur.ai/free/en/papers/habash-2006-magead-morphological-analyzer-generator-arabic/). In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, pages 681–688, Sydney, Australia. [↗](https://doi.org/10.7916/d87s7x73)
17. Nizar Habash and Fatiha Sadat. 2006. [Arabic Preprocessing Schemes for Statistical Machine Translation](https://md.research.elamur.ai/free/en/papers/habash-2006-arabic-preprocessing-schemes-statistical-machine/). In Proceedings of the 7th Meeting of the North American Chapter of the Association for Computational Linguistics/Human Language Technologies Conference (HLT-NAACL06), pages 49–52, New York, NY.
18. Nizar Habash, Abdelhadi Soudi, and Tim Buckwalter. 2007. On Arabic Transliteration. In A. van den Bosch and A. Soudi, editors, Arabic Computational Morphology: Knowledge-based and Empirical Methods. Springer. [↗](https://doi.org/10.1007/978-1-4020-6046-5_2)
19. Nizar Habash, Mona Diab, and Owen Rabmow. 2012a. [Conventional Orthography for Dialectal Arabic](https://md.research.elamur.ai/free/en/papers/habash-2012-conventional-orthography-dialectal-arabic/). In Proceedings of the Language Resources and Evaluation Conference (LREC), Istanbul.
20. Nizar Habash, Ramy Eskander, and Abdelati Hawwari. 2012b. [A Morphological Analyzer for Egyptian Arabic](https://md.research.elamur.ai/free/en/papers/habash-2012-morphological-analyzer-egyptian-arabic/). In NAACL-HLT 2012 Workshop on Computational Morphology and Phonology (SIGMOR-PHON2012), pages 1–9.
21. Nizar Habash. 2010. Introduction to Arabic Natural Language Processing. Morgan & Claypool Publishers. [↗](https://doi.org/10.1007/978-3-031-02139-8)
22. Jan Hajič. 2000. [Morphological tagging: Data vs. dictionaries](https://md.research.elamur.ai/free/en/papers/hajic-2000-morphological-tagging-data-vs-dictionaries/). In Proceedings of the 1st Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL’00), Seattle, WA.
23. Clive Holes. 2004. Modern Arabic: Structures, Functions, and Varieties. Georgetown Classics in Arabic Language and Linguistics. Georgetown University Press. [find](https://scholar.google.com/scholar?q=Clive+Holes%2E+2004%2E+Modern+Arabic%3A+Structures%2C+Functions%2C+and+Varieties%2E+Georgetown+Classics+in+Arabic+Language+and+Linguistics%2E+Georgetown+University+Press%2E)
24. H. Kilany, H. Gadalla, H. Arram, A. Yacoub, A. El-Habashi, and C. McLemore. 2002. Egyptian Colloquial Arabic Lexicon. LDC catalog number LDC99L22. [find](https://scholar.google.com/scholar?q=H%2E+Kilany%2C+H%2E+Gadalla%2C+H%2E+Arram%2C+A%2E+Yacoub%2C+A%2E+El%2DHabashi%2C+and+C%2E+McLemore%2E+2002%2E+Egyptian+Colloquial+Arabic+Lexicon%2E+LDC+catalog+number+LDC99L22%2E)
25. Philipp Koehn, Hieu Hoang, Alexandra Birch, Christopher Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Christopher Dyer, Ondrej Bojar, Alexandra Constantin, and Evan Herbst. 2007. Moses: open source toolkit for statistical machine translation. In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions, pages 177–180, Prague, Czech Republic. [find](https://scholar.google.com/scholar?q=Philipp+Koehn%2C+Hieu+Hoang%2C+Alexandra+Birch%2C+Christopher+Callison%2DBurch%2C+Marcello+Federico%2C+Nicola+Bertoldi%2C+Brooke+Cowan%2C+Wade+Shen%2C+Christine+Moran%2C+Richard+Zens%2C+Christopher+Dyer%2C+Ondrej+Bojar%2C+Alex)
26. Seth Kulick, Ryan Gabbard, and Mitch Marcus. 2006. Parsing the Arabic Treebank: Analysis and Improvements. In Proceedings of the Treebanks and Linguistic Theories Conference, pages 31–42, Prague, Czech Republic. [find](https://scholar.google.com/scholar?q=Seth+Kulick%2C+Ryan+Gabbard%2C+and+Mitch+Marcus%2E+2006%2E+Parsing+the+Arabic+Treebank%3A+Analysis+and+Improvements%2E+In+Proceedings+of+the+Treebanks+and+Linguistic+Theories+Conference%2C+pages+31%E2%80%9342%2C+Prague%2C+Czec)
27. Mohamed Maamouri, Ann Bies, Tim Buckwalter, and Wigdan Mekki. 2004. The Penn Arabic Treebank : Building a Large-Scale Annotated Arabic Corpus. In NEMLAR Conference on Arabic Language Resources and Tools, pages 102–109, Cairo, Egypt. [find](https://scholar.google.com/scholar?q=Mohamed+Maamouri%2C+Ann+Bies%2C+Tim+Buckwalter%2C+and+Wigdan+Mekki%2E+2004%2E+The+Penn+Arabic+Treebank+%3A+Building+a+Large%2DScale+Annotated+Arabic+Corpus%2E+In+NEMLAR+Conference+on+Arabic+Language+Resources+and+Too)
28. Mohamed Maamouri, Ann Bies, Tim Buckwalter, Mona Diab, Nizar Habash, Owen Rambow, and Dalila Tabessi. 2006. Developing and Using a Pilot Dialectal Arabic Treebank. In The fifth international conference on Language Resources and Evaluation (LREC), pages 443–448, Genoa, Italy. [find](https://scholar.google.com/scholar?q=Mohamed+Maamouri%2C+Ann+Bies%2C+Tim+Buckwalter%2C+Mona+Diab%2C+Nizar+Habash%2C+Owen+Rambow%2C+and+Dalila+Tabessi%2E+2006%2E+Developing+and+Using+a+Pilot+Dialectal+Arabic+Treebank%2E+In+The+fifth+international+conferenc)
29. Mohamed Maamouri, Ann Bies, Seth Kulick, Dalila Tabessi, and Sondos Krouna. 2012a. Egyptian Arabic Treebank Pilot. [find](https://scholar.google.com/scholar?q=Mohamed+Maamouri%2C+Ann+Bies%2C+Seth+Kulick%2C+Dalila+Tabessi%2C+and+Sondos+Krouna%2E+2012a%2E+Egyptian+Arabic+Treebank+Pilot%2E)
30. Mohamed Maamouri, Sondos Krouna, Dalila Tabessi, Nadia Hamrouni, and Nizar Habash. 2012b. Egyptian Arabic Morphological Annotation Guidelines. [find](https://scholar.google.com/scholar?q=Mohamed+Maamouri%2C+Sondos+Krouna%2C+Dalila+Tabessi%2C+Nadia+Hamrouni%2C+and+Nizar+Habash%2E+2012b%2E+Egyptian+Arabic+Morphological+Annotation+Guidelines%2E)
31. Emad Mohamed, Behrang Mohit, and Kemal Oflazer. 2012. Annotating and Learning Morphological Segmentation of Egyptian Colloquial Arabic. In Proceedings of the Language Resources and Evaluation Conference (LREC), Istanbul. [find](https://scholar.google.com/scholar?q=Emad+Mohamed%2C+Behrang+Mohit%2C+and+Kemal+Oflazer%2E+2012%2E+Annotating+and+Learning+Morphological+Segmentation+of+Egyptian+Colloquial+Arabic%2E+In+Proceedings+of+the+Language+Resources+and+Evaluation+Conferen)
32. Franz Josef Och. 2003. Minimum Error Rate Training for Statistical Machine Translation. In Proceedings of the 41st Annual Conference of the Association for Computational Linguistics, pages 160–167, Sapporo, Japan. [find](https://scholar.google.com/scholar?q=Franz+Josef+Och%2E+2003%2E+Minimum+Error+Rate+Training+for+Statistical+Machine+Translation%2E+In+Proceedings+of+the+41st+Annual+Conference+of+the+Association+for+Computational+Linguistics%2C+pages+160%E2%80%93167%2C+Sa)
33. Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. [BLEU: a Method for Automatic Evaluation of Machine Translation](https://md.research.elamur.ai/free/en/papers/papineni-2002-bleu-method-automatic-evaluation-machine/). In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pages 311–318, Philadelphia, PA.
34. Ryan Roth, Owen Rambow, Nizar Habash, Mona Diab, and Cynthia Rudin. 2008. [Arabic morphological tagging, diacritization, and lemmatization using lexeme models and feature ranking](https://md.research.elamur.ai/free/en/papers/roth-2008-arabic-morphological-tagging-diacritization-lemmatization/). In ACL 2008: The Conference of the Association for Computational Linguistics; Companion Volume, Short Papers, Columbus, Ohio.
35. Wael Salloum and Nizar Habash. 2011. Dialectal to Standard Arabic Paraphrasing to Improve Arabic-English Statistical Machine Translation. In Proceedings of the First Workshop on Algorithms and Resources for Modelling of Dialects and Language Varieties, pages 10–21, Edinburgh, Scotland. [↗](https://doi.org/10.7916/d8697bg2)
36. Wael Salloum and Nizar Habash. 2013. Dialectal Arabic to English Machine Translation: Pivoting through Modern Standard Arabic. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), Atlanta, GA. [find](https://scholar.google.com/scholar?q=Wael+Salloum+and+Nizar+Habash%2E+2013%2E+Dialectal+Arabic+to+English+Machine+Translation%3A+Pivoting+through+Modern+Standard+Arabic%2E+In+Proceedings+of+the+2013+Conference+of+the+North+American+Chapter+of+th)
37. Matt Snover, Bonnie J. Dorr, Richard Schwartz, Linnea Micciulla, and John Makhoul. 2006. A Study of Translation Error Rate with Targeted Human Annotation. In Proceedings of the Association for Machine Transaltion in the Americas (AMTA 2006), Cambridge, Massachusetts. [find](https://scholar.google.com/scholar?q=Matt+Snover%2C+Bonnie+J%2E+Dorr%2C+Richard+Schwartz%2C+Linnea+Micciulla%2C+and+John+Makhoul%2E+2006%2E+A+Study+of+Translation+Error+Rate+with+Targeted+Human+Annotation%2E+In+Proceedings+of+the+Association+for+Machine)
38. David Stallard, Jacob Devlin, Michael Kayser, Yoong Keok Lee, and Regina Barzilay. 2012. [Unsupervised Morphology Rivals Supervised Morphology for Arabic MT](https://md.research.elamur.ai/free/en/papers/stallard-2012-unsupervised-morphology-rivals-supervised-morphology/). In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, pages 322–327, Jeju Island, Korea.
39. Andreas Stolcke. 2002. SRILM - an Extensible Language Modeling Toolkit. In Proceedings of the International Conference on Spoken Language Processing (ICSLP), volume 2, pages 901–904, Denver, CO. [find](https://scholar.google.com/scholar?q=Andreas+Stolcke%2E+2002%2E+SRILM+%2D+an+Extensible+Language+Modeling+Toolkit%2E+In+Proceedings+of+the+International+Conference+on+Spoken+Language+Processing+%28ICSLP%29%2C+volume+2%2C+pages+901%E2%80%93904%2C+Denver%2C+CO%2E)
40. Rabih Zbib, Erika Malchiodi, Jacob Devlin, David Stallard, Spyros Matsoukas, Richard Schwartz, John Makhoul, Omar F. Zaidan, and Chris Callison-Burch. 2012. [Machine translation of arabic dialects](https://md.research.elamur.ai/free/en/papers/zbib-2012-machine-translation-arabic-dialects/). In Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 49– 59, Montréal, Canada.
41. Imed Zitouni, Jeffrey S. Sorensen, and Ruhi Sarikaya. 2006. [Maximum entropy based restoration of arabic diacritics](https://md.research.elamur.ai/free/en/papers/zitouni-2006-maximum-entropy-based-restoration-arabic/). In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, pages 577–584, Sydney, Australia.

*Updated 24 October 2026*