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# Unsupervised Morphology Rivals Supervised Morphology for Arabic MT

**Authors:** David Stallard; Jacob Devlin; Michael Kayser; Yoong Keok Lee; [Regina Barzilay](https://md.mova.elamur.ai/free/en/docs/papers/authors/regina-barzilay/) · **Year:** 2012 · **Venue:** ACL 2012 (Proceedings of the 50th ACL, Volume 2: Short Papers), pp. 322–327 **Link:** [aclanthology.org/P12-2063](https://aclanthology.org/P12-2063/) · library `aclbulk-2012-stallard-unsupervised-morphology-rivals-supervised-morphology` · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) (ACL Anthology, before 2016) — summary only

[**19** citations](https://openalex.org/works?filter=cites:W2097532276) openalex · 2026-10-18[**11** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/aclbulk-2012-stallard-unsupervised-morphology-rivals-supervised-morphology/) of the 140673 papers whose bibliographies we read[Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)](https://aclanthology.org/volumes/P12-2/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/P12-2063/)
- [PDF](https://aclanthology.org/P12-2063.pdf)
- [OpenAlex](https://openalex.org/W2097532276)

**Authors (5)**

1. [David Stallard](https://openalex.org/A5110239739) h 18 · 1298 citations · 63 works
2. [Jacob Devlin](https://openalex.org/A5057457287) h 25 · 58743 citations · 53 works
3. [Michael Kayser](https://openalex.org/A5016461590) h 5 · 355 citations · 8 works
4. [Yoong Keok Lee](https://openalex.org/A5069562499) h 10 · 814 citations · 18 works
5. [Regina Barzilay](https://openalex.org/A5010124873) [ORCID](https://orcid.org/0000-0002-2921-8201) · h 93 · 36278 citations · 422 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

The question is whether an unsupervised Arabic segmenter (the Lee et al. Bayesian model) can match supervised segmenters such as MADA and Sakhr when it feeds a hierarchical string-to-tree MT system. The authors add maximum marginal decoding (MM) over 25 Gibbs-sampling runs. Sampling alone changes 13% of token segmentations between runs; MM agrees across two runs on over 95% of tokens, cuts the induced affixes for the large MT lexicon, and raises segmentation F1 on the Arabic Treebank from a mean of 86.2% to 88.2%. On NIST MT-08 (Modern Standard Arabic) the MM segmenter gives higher BLEU than MADA on the full corpus and on the 1.3M-word subset (BLEU values are in the paper's Table 4), and on a Levantine dialect corpus it gives an 18% relative BLEU gain over no segmentation.

## Ideas

- I1. Measure run-to-run agreement before trusting any stochastic step: the paper's Table 1 counts the share of tokens and types that two runs segment alike. Mova is deterministic, so this is a check for any sampled or randomized component of `tools/hbo`: two runs must match exactly, and any sampled step must report its agreement.
- I2. Prune rare affix classes by coverage: the paper keeps only the prefixes and suffixes that cover 95% of the affix tokens (Top-95) and sees the inventory shrink. In `tools/hbo`, the same count over the `pre` and `suf` tables shows which rare splits can be dropped.
- I3. Score a segmenter by its downstream effect, not only its F1: the paper reports the segmenter's gain in MT BLEU as well as its segmentation accuracy. For Mova, report `gparse` LAS on `treebanks/hbo/ptnk` next to each segmentation variant, and keep the test set held out until the release.

## For Mova

I1 and I3 are the measurement rules for `tools/hbo` (segmenter plus `gparse`). I2 is a table audit for the segmenter tables `pre` and `suf` in `tools/hbo`. The unsupervised model itself is not a Mova idea: Mova's segmenter learns its splits from MACULA rather than sampling them.

Topics: [Treebanks](https://md.mova.elamur.ai/free/en/docs/papers/topics/treebanks/) · [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/)

## Cite

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

```
@inproceedings{stallard2012unsupervised,
  title = {{Unsupervised Morphology Rivals Supervised Morphology for Arabic MT}},
  author = {Stallard, David and Devlin, Jacob and Kayser, Michael and Lee, Yoong Keok and Barzilay, Regina},
  booktitle = {Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  year = {2012},
  url = {https://aclanthology.org/P12-2063/}
}
```

- [← previous Conversion of Procedural Morphologies to Finite-State Morphologies: A Case Study of Arabic](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2012-hulden-conversion-procedural-morphologies-finite-state/)
- [next → Arabic Morphological Analyzer with Agglutinative Affix Morphemes and Fusional Concatenation Rules](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2012-zaraket-arabic-morphological-analyzer-agglutinative-affix/)
- [↑ back to Semitic languages](https://md.mova.elamur.ai/free/en/docs/papers/sem/)

## References

26 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Eleftherios Avramidis and Philipp Koehn. 2008. Enriching morphologically poor languages for statistical machine translation. In Proceedings of ACL-08: HLT. [find](https://scholar.google.com/scholar?q=Eleftherios+Avramidis+and+Philipp+Koehn%2E+2008%2E+Enriching+morphologically+poor+languages+for+statistical+machine+translation%2E+In+Proceedings+of+ACL%2D08%3A+HLT%2E)
2. Ibrahim Badr, Rabih Zbib, and James Glass. 2008. Segmentation for English-to-Arabic statistical machine translation. In Proceedings of ACL-08: HLT, Short Papers. [find](https://scholar.google.com/scholar?q=Ibrahim+Badr%2C+Rabih+Zbib%2C+and+James+Glass%2E+2008%2E+Segmentation+for+English%2Dto%2DArabic+statistical+machine+translation%2E+In+Proceedings+of+ACL%2D08%3A+HLT%2C+Short+Papers%2E)
3. Ann Clifton and Anoop Sarkar. 2011. Combining morpheme-based machine translation with post-processing morpheme prediction. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. [find](https://scholar.google.com/scholar?q=Ann+Clifton+and+Anoop+Sarkar%2E+2011%2E+Combining+morpheme%2Dbased+machine+translation+with+post%2Dprocessing+morpheme+prediction%2E+In+Proceedings+of+the+49th+Annual+Meeting+of+the+Association+for+Computationa)
4. Mathias Creutz and Krista Lagus. 2007. Unsupervised models for morpheme segmentation and morphology learning. ACM Trans. Speech Lang. Process., 4:3:1– 3:34, February. [find](https://scholar.google.com/scholar?q=Mathias+Creutz+and+Krista+Lagus%2E+2007%2E+Unsupervised+models+for+morpheme+segmentation+and+morphology+learning%2E+ACM+Trans%2E+Speech+Lang%2E+Process%2E%2C+4%3A3%3A1%E2%80%93+3%3A34%2C+February%2E)
5. Nizar Habash and Owen Rambow. 2005. Arabic tokenization, part-of-speech tagging and morphological disambiguation in one fell swoop. In Proceedings of ACL. [find](https://scholar.google.com/scholar?q=Nizar+Habash+and+Owen+Rambow%2E+2005%2E+Arabic+tokenization%2C+part%2Dof%2Dspeech+tagging+and+morphological+disambiguation+in+one+fell+swoop%2E+In+Proceedings+of+ACL%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2005-habash-arabic-tokenization-part-speech-tagging/)
6. Nizar Habash. 2008. Four techniques for online handling of out-of-vocabulary words in Arabic-English statistical machine translation. In Proceedings of ACL-08: HLT, Short Papers. [find](https://scholar.google.com/scholar?q=Nizar+Habash%2E+2008%2E+Four+techniques+for+online+handling+of+out%2Dof%2Dvocabulary+words+in+Arabic%2DEnglish+statistical+machine+translation%2E+In+Proceedings+of+ACL%2D08%3A+HLT%2C+Short+Papers%2E)
7. Mark Johnson and Sharon Goldwater. 2009. Improving nonparametric bayesian inference: experiments on unsupervised word segmentation with adaptor grammars. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Mark+Johnson+and+Sharon+Goldwater%2E+2009%2E+Improving+nonparametric+bayesian+inference%3A+experiments+on+unsupervised+word+segmentation+with+adaptor+grammars%2E+In+Proceedings+of+Human+Language+Technologies%3A)
8. Philipp Koehn and Hieu Hoang. 2007. Factored translation models. In Proceedings of EMNLP-CoNLL, pages 868–876. [find](https://scholar.google.com/scholar?q=Philipp+Koehn+and+Hieu+Hoang%2E+2007%2E+Factored+translation+models%2E+In+Proceedings+of+EMNLP%2DCoNLL%2C+pages+868%E2%80%93876%2E)
9. Philipp Koehn. 2004. Statistical significance tests for machine translation evaluation. In Proceedings of EMNLP 2004. [find](https://scholar.google.com/scholar?q=Philipp+Koehn%2E+2004%2E+Statistical+significance+tests+for+machine+translation+evaluation%2E+In+Proceedings+of+EMNLP+2004%2E)
10. Yoong Keok Lee, Aria Haghighi, and Regina Barzilay. 2011. Modeling syntactic context improves morphological segmentation. In Proceedings of the Fifteenth Conference on Computational Natural Language Learning. [find](https://scholar.google.com/scholar?q=Yoong+Keok+Lee%2C+Aria+Haghighi%2C+and+Regina+Barzilay%2E+2011%2E+Modeling+syntactic+context+improves+morphological+segmentation%2E+In+Proceedings+of+the+Fifteenth+Conference+on+Computational+Natural+Language+L)
11. Minh-Thang Luong, Preslav Nakov, and Min-Yen Kan. 2010. A hybrid morpheme-word representation for machine translation of morphologically rich languages. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing. [find](https://scholar.google.com/scholar?q=Minh%2DThang+Luong%2C+Preslav+Nakov%2C+and+Min%2DYen+Kan%2E+2010%2E+A+hybrid+morpheme%2Dword+representation+for+machine+translation+of+morphologically+rich+languages%2E+In+Proceedings+of+the+2010+Conference+on+Empiri)
12. Coşkun Mermer and Ahmet Afşın Akın. 2010. Unsupervised search for the optimal segmentation for statistical machine translation. In Proceedings of the ACL 2010 Student Research Workshop, pages 31–36, Uppsala, Sweden, July. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Cos%CC%A7kun+Mermer+and+Ahmet+Afs%CC%A7%C4%B1n+Ak%C4%B1n%2E+2010%2E+Unsupervised+search+for+the+optimal+segmentation+for+statistical+machine+translation%2E+In+Proceedings+of+the+ACL+2010+Student+Research+Workshop%2C+pages+31%E2%80%9336%2C)
13. Coşkun Mermer and Murat Saraclar. 2011. Unsupervised Turkish morphological segmentation for statistical machine translation. In Workshop on Machine Translation and Morphologically-rich languages, January. [find](https://scholar.google.com/scholar?q=Cos%CC%A7kun+Mermer+and+Murat+Saraclar%2E+2011%2E+Unsupervised+Turkish+morphological+segmentation+for+statistical+machine+translation%2E+In+Workshop+on+Machine+Translation+and+Morphologically%2Drich+languages%2C+Jan)
14. Preslav Nakov and Hwee Tou Ng. 2011. Translating from morphologically complex languages: A paraphrase-based approach. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. [find](https://scholar.google.com/scholar?q=Preslav+Nakov+and+Hwee+Tou+Ng%2E+2011%2E+Translating+from+morphologically+complex+languages%3A+A+paraphrase%2Dbased+approach%2E+In+Proceedings+of+the+49th+Annual+Meeting+of+the+Association+for+Computational+Lin)
15. Jason Naradowsky and Kristina Toutanova. 2011. Unsupervised bilingual morpheme segmentation and alignment with context-rich hidden semi-Markov models. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. [find](https://scholar.google.com/scholar?q=Jason+Naradowsky+and+Kristina+Toutanova%2E+2011%2E+Unsupervised+bilingual+morpheme+segmentation+and+alignment+with+context%2Drich+hidden+semi%2DMarkov+models%2E+In+Proceedings+of+the+49th+Annual+Meeting+of+the+)
16. NIST. 2010. NIST 2008 Open Machine Translation (Open MT) Evaluation. [http://www.ldc.upenn.edu/Catalog/catalogEntry.jsp?](http://www.ldc.upenn.edu/Catalog/catalogEntry.jsp?) catalogId=LDC2010T21/. [↗](http://www.ldc.upenn.edu/Catalog/catalogEntry.jsp?)
17. Kemal Oflazer. 1993. Two-level description of Turkish morphology. In Proceedings of the Sixth Conference of the European Chapter of the Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Kemal+Oflazer%2E+1993%2E+Two%2Dlevel+description+of+Turkish+morphology%2E+In+Proceedings+of+the+Sixth+Conference+of+the+European+Chapter+of+the+Association+for+Computational+Linguistics%2E)
18. Hoifung Poon, Colin Cherry, and Kristina Toutanova. 2009. Unsupervised morphological segmentation with log-linear models. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Hoifung+Poon%2C+Colin+Cherry%2C+and+Kristina+Toutanova%2E+2009%2E+Unsupervised+morphological+segmentation+with+log%2Dlinear+models%2E+In+Proceedings+of+Human+Language+Technologies%3A+The+2009+Annual+Conference+of+t)
19. Lawrence R. Rabiner. 1989. A tutorial on hidden Markov models and selected applications in speech recognition. In Proceedings of the IEEE, pages 257– 286. [find](https://scholar.google.com/scholar?q=Lawrence+R%2E+Rabiner%2E+1989%2E+A+tutorial+on+hidden+Markov+models+and+selected+applications+in+speech+recognition%2E+In+Proceedings+of+the+IEEE%2C+pages+257%E2%80%93+286%2E)
20. Fatiha Sadat and Nizar Habash. 2006. Combination of Arabic preprocessing schemes for statistical machine translation. In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Fatiha+Sadat+and+Nizar+Habash%2E+2006%2E+Combination+of+Arabic+preprocessing+schemes+for+statistical+machine+translation%2E+In+Proceedings+of+the+21st+International+Conference+on+Computational+Linguistics+a)
21. Libin Shen, Jinxi Xu, and Ralph Weischedel. 2008. A new string-to-dependency machine translation algorithm with a target dependency language model. In Proceedings of ACL-08: HLT. [find](https://scholar.google.com/scholar?q=Libin+Shen%2C+Jinxi+Xu%2C+and+Ralph+Weischedel%2E+2008%2E+A+new+string%2Dto%2Ddependency+machine+translation+algorithm+with+a+target+dependency+language+model%2E+In+Proceedings+of+ACL%2D08%3A+HLT%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/shen-2008-new-string-dependency-machine-translation/)
22. Sami Virpioja, Jaakko J. Väyrynen, Mathias Creutz, and Markus Sadeniemi. 2007. Morphology-aware statistical machine translation based on morphs induced in an unsupervised manner. In Proceedings of the Machine Translation Summit XI. [find](https://scholar.google.com/scholar?q=Sami+Virpioja%2C+Jaakko+J%2E+Va%CC%88yrynen%2C+Mathias+Creutz%2C+and+Markus+Sadeniemi%2E+2007%2E+Morphology%2Daware+statistical+machine+translation+based+on+morphs+induced+in+an+unsupervised+manner%2E+In+Proceedings+of+th)
23. Larry Wasserman. 2006. All of Nonparametric Statistics. Springer. [find](https://scholar.google.com/scholar?q=Larry+Wasserman%2E+2006%2E+All+of+Nonparametric+Statistics%2E+Springer%2E)
24. Mei Yang and Katrin Kirchhoff. 2006. Phrase-based backoff models for machine translation of highly inflected languages. In Proceedings of EACL. [find](https://scholar.google.com/scholar?q=Mei+Yang+and+Katrin+Kirchhoff%2E+2006%2E+Phrase%2Dbased+backoff+models+for+machine+translation+of+highly+inflected+languages%2E+In+Proceedings+of+EACL%2E)
25. Rabih Zbib, Michael Kayser, Spyros Matsoukas, John Makhoul, Hazem Nader, Hamdy Soliman, and Rami Safadi. 2012a. Methods for integrating rule-based and statistical systems for Arabic to English machine translation. Machine Translation, 26(1-2):67–83. [find](https://scholar.google.com/scholar?q=Rabih+Zbib%2C+Michael+Kayser%2C+Spyros+Matsoukas%2C+John+Makhoul%2C+Hazem+Nader%2C+Hamdy+Soliman%2C+and+Rami+Safadi%2E+2012a%2E+Methods+for+integrating+rule%2Dbased+and+statistical+systems+for+Arabic+to+English+machine)
26. Rabih Zbib, Erika Malchiodi, Jacob Devlin, David Stallard, Spyros Matsoukas, Richard Schwartz, John Makhoul, Omar F. Zaidan, and Chris Callison-Burch. 2012b. Machine translation of Arabic dialects. In NAACL 2012: Proceedings of the 2012 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, Montreal, Quebec, Canada, June. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Rabih+Zbib%2C+Erika+Malchiodi%2C+Jacob+Devlin%2C+David+Stallard%2C+Spyros+Matsoukas%2C+Richard+Schwartz%2C+John+Makhoul%2C+Omar+F%2E+Zaidan%2C+and+Chris+Callison%2DBurch%2E+2012b%2E+Machine+translation+of+Arabic+dialects%2E+In)