---
title: "Dudley North visits North London — paper by Azab et al. (2013)"
description: "When translating English into Arabic, a named-entity token must either be rendered by… 90% accuracy. 20 references, cited by 5 papers here."
authors: ["Mahmoud Azab", "Houda Bouamor", "Behrang Mohit", "Kemal Oflazer"]
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/N13-1046/"
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/azab-2013-dudley-north-visits-north-london/"
---
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# Dudley North visits North London: Learning When to Transliterate to Arabic

**Authors:** [Mahmoud Azab](https://md.research.elamur.ai/free/en/authors/mahmoud-azab/), [Houda Bouamor](https://md.research.elamur.ai/free/en/authors/houda-bouamor/), [Behrang Mohit](https://md.research.elamur.ai/free/en/authors/behrang-mohit/) and [Kemal Oflazer](https://md.research.elamur.ai/free/en/authors/kemal-oflazer/) · **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/N13-1046/) · [PDF](https://aclanthology.org/N13-1046.pdf)

**Citations:** [5](https://md.research.elamur.ai/free/en/papers/azab-2013-dudley-north-visits-north-london/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
When translating English into Arabic, a named-entity token must either be rendered by meaning (translated) or by sound (transliterated). Ordinary words inside names cause trouble: "North" in "Dudley North" should be transliterated, but in "North London" it should be translated. The authors treat this choice as a token-level binary classification task. They first built labeled bilingual NE lexicons automatically. About 57K pairs come from a word-aligned news parallel corpus, and about 4K come from Wikipedia titles. Labels were assigned with a transliteration-similarity score, and only the uncertain cases received manual handling. An SVM classifier over 32 features reaches roughly 90% accuracy on news and up to 86% on Wikipedia, compared with a majority-class baseline near 57%. Inside a Moses English→Arabic system, the classifier gives a small but significant BLEU gain. On diverse Wikipedia text, it routes most of the would-be-mistranslated name tokens to transliteration. The work shows that a cheap, explicit decision layer can repair a systematic failure of MT on names.

## Key points
- **Data construction**
  - English sides of a parallel corpus were tagged with the UIUC NE tagger (PER/LOC/ORG/MISC).
  - Arabic spans were projected through word alignments and cleaned with POS checks and heuristic filters, yielding about 57K news NE pairs.
  - A separate 4K Wikipedia lexicon contains only entities absent from the parallel corpus.
- **Labeling**
  - Labels come from the Freeman et al. similarity score: above 0.6 means transliteration, below 0.5 means translation.
  - The roughly 5K unique tokens in between were transliterated with Microsoft Maren and then rescored.
- **Classifier and accuracy**
  - The model is LIBSVM with token-based, semantic and contextual features.
  - C_news scores 90.40 on Test_News and 84.10 on Test_Wiki.
  - C_diverse (55K news plus 4.6K Wikipedia training NEs) scores 90.42 and 86.00.
  - The baseline is about 57.
- **Feature importance:** the NE category feature contributes more than the token's local context.
- **Extrinsic evaluation**
  - In 10 Wikipedia articles (98,197 terms), 624 of 1031 NE tokens (60.5%) that MT would have translated wrongly were sent to transliteration.
  - On the MEDAR test set, BLEU rose from 16.63 to 16.91.
  - Classifier accuracy against gold labels on that set was 89%.

## Interesting ideas
- Labels are produced automatically by a similarity score, and only the ambiguous middle band between two thresholds goes to a human; this concentrates costly annotation where it matters most.
- The same surface word ("North") is handled differently depending on the type of entity it sits in, and entity category beats local context as evidence.
- Membership of a token in WordNet as a regular noun serves as a signal against treating it as a pure name.
- The out-of-domain test set was deliberately built from entities that never occur in the training corpus, so it measures domain shift honestly instead of rewarding memorization.
- The transliteration is offered to the decoder as an extra option rather than forced, so the downstream model keeps the final say.

## For Mova

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

- **I1** (learn): Silver-data agreement could use the same two-threshold scheme, auto-accepting high-agreement and auto-rejecting low-agreement parses while sending only the middle band to the LLM teacher tier.
- **I2** (world): When building entities, the decision whether a capitalized common word is part of a name or a literal descriptor (a direction, a title) could be conditioned on the entity's category rather than only on neighboring words.
- **I3** (tokens): The tagger and lemmatizer could use WordNet common-noun membership as an explainable feature when deciding between PROPN and NOUN for capitalized tokens inside multiword names.
- **I4** (learn): Evaluation of gparse or of frame extraction could include held-out splits filtered to unseen lemmas or entities, acting as a control against lexical memorization.
- **I5** (back): During translation through the meta-world, name renderings from a translate-vs-transliterate decision could be passed to generation as ranked alternatives with their justification, preserving explainability.

*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): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works

🧭 Topics (1): [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · 2,908 works

## Cite

Cite as: Mahmoud Azab, Houda Bouamor, Behrang Mohit and Kemal Oflazer. 2013. Dudley North visits North London: Learning When to Transliterate to 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{azab2013dudley,
  title = {{Dudley North visits North London: Learning When to Transliterate to Arabic}},
  author = {Mahmoud Azab, Houda Bouamor, Behrang Mohit and Kemal Oflazer},
  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/N13-1046/}
}
```

- [← previous Statistical-based System for Morphological Annotation of Arabic Texts](https://md.research.elamur.ai/free/en/papers/khoufi-2013-statistical-based-system-morphological-annotation/)
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- [↑ back to papers of 2013](https://md.research.elamur.ai/free/en/papers/2013/page/3/)

## References

20 references, 7 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Yaser Al-Onaizan and Kevin Knight. 2002a. Named-Entity translation. In Proceedings of HLT, San Francisco, USA. [find](https://scholar.google.com/scholar?q=Yaser+Al%2DOnaizan+and+Kevin+Knight%2E+2002a%2E+Named%2DEntity+translation%2E+In+Proceedings+of+HLT%2C+San+Francisco%2C+USA%2E)
2. Yaser Al-Onaizan and Kevin Knight. 2002b. [Translating Named Entities Using Monolingual and Bilingual Resources](https://md.research.elamur.ai/free/en/papers/onaizan-2002-translating-named-entities-monolingual-bilingual/). In Proceedings of ACL, Philadelphia, USA.
3. Chih-Chung Chang and Chih-Jen Lin. 2011. LIB-SVM: A Library for Support Vector Machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1–27:27. Software available at [http://www](http://www). csie.ntu.edu.tw/˜cjlin/libsvm. [↗](http://www)
4. Silviu Cucerzan. 2007. Large-Scale Named-Entity Disambiguation Based on Wikipedia Data. In Proceedings of EMNLP-CoNLL, Prague, Czech Republic. [find](https://scholar.google.com/scholar?q=Silviu+Cucerzan%2E+2007%2E+Large%2DScale+Named%2DEntity+Disambiguation+Based+on+Wikipedia+Data%2E+In+Proceedings+of+EMNLP%2DCoNLL%2C+Prague%2C+Czech+Republic%2E)
5. Ahmed El Kholy and Nizar Habash. 2010. Techniques for Arabic Morphological Detokenization and Orthographic Denormalization. In Proceedings of LREC, Valletta, Malta. [find](https://scholar.google.com/scholar?q=Ahmed+El+Kholy+and+Nizar+Habash%2E+2010%2E+Techniques+for+Arabic+Morphological+Detokenization+and+Orthographic+Denormalization%2E+In+Proceedings+of+LREC%2C+Valletta%2C+Malta%2E)
6. Christiane Fellbaum. 1998. WordNet: An Electronic Lexical Database. The MIT Press. [↗](https://doi.org/10.7551/mitpress/7287.001.0001)
7. Andrew Freeman, Sherri Condon, and Christopher Ackerman. 2006. [Cross Linguistic Name Matching in English and Arabic](https://md.research.elamur.ai/free/en/papers/freeman-2006-cross-linguistic-name-matching-english/). In Proceedings of NAACL, New York City, USA.
8. Bonnie Glover-Stalls and Kevin Knight. 1998. Translating Named and Technical Terms in Arabic Text. In Proceeding of the COLING/ACL Workshop on Computational Approaches to Semitic Languages, Montreal, Canada. [find](https://scholar.google.com/scholar?q=Bonnie+Glover%2DStalls+and+Kevin+Knight%2E+1998%2E+Translating+Named+and+Technical+Terms+in+Arabic+Text%2E+In+Proceeding+of+the+COLING%2FACL+Workshop+on+Computational+Approaches+to+Semitic+Languages%2C+Montreal%2C+)
9. Nizar Habash, Owen Rambow, and Ryan Roth. 2009. Mada+Tokan: A Toolkit for Arabic Tokenization, Diacritization, Morphological Disambiguation, POS Tagging, Stemming and Lemmatization. In Proceedings of the Second International Conference on Arabic Language Resources and Tools (MEDAR), Cairo, Egypt. [find](https://scholar.google.com/scholar?q=Nizar+Habash%2C+Owen+Rambow%2C+and+Ryan+Roth%2E+2009%2E+Mada%2BTokan%3A+A+Toolkit+for+Arabic+Tokenization%2C+Diacritization%2C+Morphological+Disambiguation%2C+POS+Tagging%2C+Stemming+and+Lemmatization%2E+In+Proceedings+of+)
10. Ahmed Hassan, Haytham Fahmy, and Hany Hassan. 2007. Improving Named Entity Translation by Exploiting Comparable and Parallel Corpora. In Proceedings of RANLP, Borovets, Bulgaria. [find](https://scholar.google.com/scholar?q=Ahmed+Hassan%2C+Haytham+Fahmy%2C+and+Hany+Hassan%2E+2007%2E+Improving+Named+Entity+Translation+by+Exploiting+Comparable+and+Parallel+Corpora%2E+In+Proceedings+of+RANLP%2C+Borovets%2C+Bulgaria%2E)
11. Ulf Hermjakob, Kevin Knight, and Hal Daumé III. 2008. Name Translation in Statistical Machine Translation - Learning When to Transliterate. In Proceedings of ACL-HLT, Columbus, Ohio. [find](https://scholar.google.com/scholar?q=Ulf+Hermjakob%2C+Kevin+Knight%2C+and+Hal+Daume%CC%81+III%2E+2008%2E+Name+Translation+in+Statistical+Machine+Translation+%2D+Learning+When+to+Transliterate%2E+In+Proceedings+of+ACL%2DHLT%2C+Columbus%2C+Ohio%2E)
12. Jun’ichi Kazama and Kentaro Torisawa. 2007. [Exploiting Wikipedia as External Knowledge for Named-Entity Recognition](https://md.research.elamur.ai/free/en/papers/kazama-2007-exploiting-wikipedia-as-external-knowledge/). In Proceedings of EMNLP-CoNLL, Prague, Czech Republic.
13. Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Chris Dyer, Ondrej Bojar, Alexandra Constantin, and Evan Herbst. 2007. Moses: Open Source Toolkit for Statistical Machine Translation. In Proceedings of ACL: Demo session, Prague, Czech Republic. [find](https://scholar.google.com/scholar?q=Philipp+Koehn%2C+Hieu+Hoang%2C+Alexandra+Birch%2C+Chris+Callison%2DBurch%2C+Marcello+Federico%2C+Nicola+Bertoldi%2C+Brooke+Cowan%2C+Wade+Shen%2C+Christine+Moran%2C+Richard+Zens%2C+Chris+Dyer%2C+Ondrej+Bojar%2C+Alexandra+Consta)
14. Wang Ling, Pavel Calado, Bruno Martins, Isabel Trancoso, and Alan Black. 2011. Named-Entity Translation using Anchor Texts. In Proceedings of IWSLT, San Francisco, USA. [find](https://scholar.google.com/scholar?q=Wang+Ling%2C+Pavel+Calado%2C+Bruno+Martins%2C+Isabel+Trancoso%2C+and+Alan+Black%2E+2011%2E+Named%2DEntity+Translation+using+Anchor+Texts%2E+In+Proceedings+of+IWSLT%2C+San+Francisco%2C+USA%2E)
15. Bente Maegaard, Mohamed Attia, Khalid Choukri, Olivier Hamon, Steven Krauwer, and Mustafa Yaseen. 2010. [Cooperation for Arabic Language Resources and Tools–The MEDAR Project](https://md.research.elamur.ai/free/en/papers/maegaard-2010-cooperation-arabic-language-resources-tools/). In Proceedings of LREC, Valetta, Malta.
16. Behrang Mohit, Nathan Schneider, Rishav Bhowmick, Kemal Oflazer, and Noah A. Smith. 2012. [Recall-Oriented Learning of Named Entities in Arabic Wikipedia](https://md.research.elamur.ai/free/en/papers/mohit-2012-recall-oriented-learning-named-entities/). In Proceedings of EACL, Avignon, France.
17. Joel Nothman, Nicky Ringland, Will Radford, Tara Murphy, and James R. Curran. 2013. Learning Multilingual Named Entity Recognition from Wikipedia. Artificial Intelligence, 194(0):151 – 175. [↗](https://doi.org/10.1016/j.artint.2012.03.006)
18. Franz Josef Och. 2003. Minimum Error Rate Training in Statistical Machine Translation. In Proceedings of ACL, Sapporo, Japan. [find](https://scholar.google.com/scholar?q=Franz+Josef+Och%2E+2003%2E+Minimum+Error+Rate+Training+in+Statistical+Machine+Translation%2E+In+Proceedings+of+ACL%2C+Sapporo%2C+Japan%2E)
19. 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 ACL, Philadelphia, USA.
20. Lev Ratinov and Dan Roth. 2009. [Design Challenges and Misconceptions in Named Entity Recognition](https://md.research.elamur.ai/free/en/papers/ratinov-2009-design-challenges-misconceptions-named-entity/). In Proceedings of CONLL, Boulder, USA.

*Updated 1 November 2026*