---
title: "Applying Morphology Generation Models to Machine Translation"
description: "The authors build a standalone inflection generator, a Maximum Entropy Markov model following… 17 references, cited by 63 papers here."
authors: ["Kristina Toutanova", "Hisami Suzuki", "Achim Ruopp"]
year: 2008
venue: "Proceedings of ACL-08: HLT"
source: "https://aclanthology.org/P08-1059/"
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/toutanova-2008-applying-morphology-generation-models-machine/"
---
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# Applying Morphology Generation Models to Machine Translation

**Authors:** [Kristina Toutanova](https://md.research.elamur.ai/free/en/authors/kristina-toutanova/), [Hisami Suzuki](https://md.research.elamur.ai/free/en/authors/hisami-suzuki/) and Achim Ruopp · **Year:** 2008 · **Venue:** [Proceedings of ACL-08: HLT](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2008](https://md.research.elamur.ai/free/en/venues/acl/2008/)) · **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/P08-1059/) · [PDF](https://aclanthology.org/P08-1059.pdf) · [OpenAlex](https://openalex.org/W2117642127)

**Citations:** 121 citations (OpenAlex, 2026-10-14) · [63](https://md.research.elamur.ai/free/en/papers/toutanova-2008-applying-morphology-generation-models-machine/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary

The paper tackles a common weakness of statistical MT when translating from English into languages with rich inflection: the system often picks the right word but the wrong form, so agreement and case are frequently broken. The authors build a standalone inflection generator, a Maximum Entropy Markov model following Minkov et al. (2007). It takes a sequence of target stems and selects a surface form for each one, using a wide range of features from the English source (POS tags, dependency structure, alignments) and from the target side. The model is trained separately from the translation system. It is then combined with a syntax-based treelet system and a phrasal system for English→Russian and English→Arabic. The authors compare several ways of joining the two parts, including running the base MT on full word forms or on stems, and passing n-best lists between the components. Every setup they test improves BLEU, and native-speaker judges also prefer the output. This shows that a separately built morphology component can improve MT systems of different architectures.

## Key points

- **Inflection accuracy on clean input:** Given stemmed reference translations, the model chooses the correct form 91.6% of the time for Russian and 91.0% for Arabic. A trigram LM baseline reaches only 81.0% and 69.4%. Each stem has roughly 14 possible forms in Russian and 24 in Arabic.
- **Lexicons and features:**
  - The Russian lexicon was built by intersecting a general lexicon with the training data. Russian uses 7 morphological features and treats capitalization as an extra inflectional feature.
  - The Arabic lexicon comes from the Buckwalter analyser. Arabic uses 12 features.
  - No contextual disambiguation was applied in either language.
- **English→Arabic treelet system:** BLEU rose from 35.54 to 37.41 (Method 1, n=2). The variants built around stems raised the oracle BLEU but did not raise the BLEU actually obtained.
- **English→Russian:** The system was trained on about 1.6M sentence pairs.
  - In the treelet system, BLEU gains reached up to 2.6.
  - In the phrasal system, which had a much stronger baseline, BLEU went from 36.00 to 36.72 using 100-best input.
- **Human evaluation:** Two native judges compared 100 sentences per scenario. All average scores favored the new model: 0.1 for Arabic, and 0.255 and 0.26 for the two Russian setups. The Arabic gain was not statistically significant.

## Interesting ideas
- **Keep morphology outside the decoder.** A separate post-model is not limited by the decoder's phrase boundaries or context window, so it can use features from anywhere in the sentence. The same component also works with very different MT systems. This design suits any rich-feature correction step, such as case or agreement repair for Ukrainian.
- **Pass n-best lists between components instead of a single best guess.** Carrying alternative translations into the inflection step improved results compared with a 1-best handoff. It is a cheap way to reduce error propagation in a pipeline.
- **Test the component on gold input first.** Measuring inflection accuracy on stemmed reference translations gives an upper bound on what the module can do in isolation. This helps separate the component's own errors from errors caused by noisy MT output.
- **Fit stemming to the language pair.** The authors suspect stemming hurt Arabic because it removed distinctions that English marks explicitly, such as definiteness and clitics. Normalization should only collapse what the source language leaves unmarked, which is a useful rule when designing lexicons for other language pairs.
- **Check BLEU against human judgement.** Two Russian setups had very different BLEU gains (1.2 vs 2.6) but almost the same human preference scores. Small, targeted changes such as inflection fixes deserve direct human evaluation.

*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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- [Microsoft](https://md.research.elamur.ai/free/en/companies/microsoft/) — Company, Redmond, United States · 240 works · 123,537 citations

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## Cite

Cite as: Kristina Toutanova, Hisami Suzuki and Achim Ruopp. 2008. Applying Morphology Generation Models to Machine Translation. Proceedings of ACL-08: HLT.

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

```bibtex
@inproceedings{toutanova2008applying,
  title = {{Applying Morphology Generation Models to Machine Translation}},
  author = {Kristina Toutanova, Hisami Suzuki and Achim Ruopp},
  booktitle = {Proceedings of ACL-08: HLT},
  year = {2008},
  url = {https://aclanthology.org/P08-1059/}
}
```

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- [↑ back to papers of 2008](https://md.research.elamur.ai/free/en/papers/2008/)

## References

17 references, 4 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Tim Buckwalter. 2004. Buckwalter arabic morphological analyzer version 2.0. [find](https://scholar.google.com/scholar?q=Tim+Buckwalter%2E+2004%2E+Buckwalter+arabic+morphological+analyzer+version+2%2E0%2E)
2. Ilknur Durgar El-Kahlout and Kemal Oflazer. 2006. Initial explorations in English to Turkish statistical machine translation. In NAACL workshop on statistical machine translation. [find](https://scholar.google.com/scholar?q=Ilknur+Durgar+El%2DKahlout+and+Kemal+Oflazer%2E+2006%2E+Initial+explorations+in+English+to+Turkish+statistical+machine+translation%2E+In+NAACL+workshop+on+statistical+machine+translation%2E)
3. Jenny Finkel, Christopher Manning, and Andrew Ng. 2006. Solving the problem of cascading errors: approximate Bayesian inference for linguistic annotation pipelines. In EMNLP. [find](https://scholar.google.com/scholar?q=Jenny+Finkel%2C+Christopher+Manning%2C+and+Andrew+Ng%2E+2006%2E+Solving+the+problem+of+cascading+errors%3A+approximate+Bayesian+inference+for+linguistic+annotation+pipelines%2E+In+EMNLP%2E)
4. Alexander Fraser and Daniel Marcu. 2007. Measuring word alignment quality for statistical machine translation. Computational Linguistics, 33(3):293–303. [find](https://scholar.google.com/scholar?q=Alexander+Fraser+and+Daniel+Marcu%2E+2007%2E+Measuring+word+alignment+quality+for+statistical+machine+translation%2E+Computational+Linguistics%2C+33%283%29%3A293%E2%80%93303%2E)
5. Sharon Goldwater and David McClosky. 2005. [Improving statistical MT through morphological analysis](https://md.research.elamur.ai/free/en/papers/goldwater-2005-improving-statistical-mt-through-morphological/). In EMNLP.
6. 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 HLT-NAACL.
7. Xiaodong He. 2007. Using word-dependent transition models in HMM based word alignment for statistical machine translation. In ACL Workshop on Statistical Machine Translation. [find](https://scholar.google.com/scholar?q=Xiaodong+He%2E+2007%2E+Using+word%2Ddependent+transition+models+in+HMM+based+word+alignment+for+statistical+machine+translation%2E+In+ACL+Workshop+on+Statistical+Machine+Translation%2E)
8. Philipp Koehn and Hieu Hoang. 2007. [Factored translation models](https://md.research.elamur.ai/free/en/papers/koehn-2007-factored-translation-models/). In EMNLP-CoNNL.
9. Philipp Koehn. 2004. Pharaoh: a beam search decoder for phrase-based statistical machine translation models. In AMTA. [find](https://scholar.google.com/scholar?q=Philipp+Koehn%2E+2004%2E+Pharaoh%3A+a+beam+search+decoder+for+phrase%2Dbased+statistical+machine+translation+models%2E+In+AMTA%2E)
10. Young-Suk Lee. 2004. [Morphological analysis for statistical machine translation](https://md.research.elamur.ai/free/en/papers/lee-2004-morphological-analysis-statistical-machine-translation/). In HLT-NAACL.
11. Einat Minkov, Kristina Toutanova, and Hisami Suzuki. 2007. Generating complex morphology for machine translation. In ACL. [find](https://scholar.google.com/scholar?q=Einat+Minkov%2C+Kristina+Toutanova%2C+and+Hisami+Suzuki%2E+2007%2E+Generating+complex+morphology+for+machine+translation%2E+In+ACL%2E)
12. Sonja Nießen and Hermann Ney. 2004. Statistical machine translation with scarce resources using morphosyntactic information. Computational Linguistics, 30(2):181–204. [find](https://scholar.google.com/scholar?q=Sonja+Nie%C3%9Fen+and+Hermann+Ney%2E+2004%2E+Statistical+machine+translation+with+scarce+resources+using+morphosyntactic+information%2E+Computational+Linguistics%2C+30%282%29%3A181%E2%80%93204%2E)
13. Franz Och. 2003. Minimum error rate training for statistical machine translation. In ACL. [find](https://scholar.google.com/scholar?q=Franz+Och%2E+2003%2E+Minimum+error+rate+training+for+statistical+machine+translation%2E+In+ACL%2E)
14. Chris Quirk, Arul Menezes, and Colin Cherry. 2005. Dependency tree translation: Syntactically informed phrasal SMT. In ACL. [find](https://scholar.google.com/scholar?q=Chris+Quirk%2C+Arul+Menezes%2C+and+Colin+Cherry%2E+2005%2E+Dependency+tree+translation%3A+Syntactically+informed+phrasal+SMT%2E+In+ACL%2E)
15. Kristina Toutanova and Hisami Suzuki. 2007. Generating case markers in machine translation. In NAACL-HLT. [find](https://scholar.google.com/scholar?q=Kristina+Toutanova+and+Hisami+Suzuki%2E+2007%2E+Generating+case+markers+in+machine+translation%2E+In+NAACL%2DHLT%2E)
16. Vladimir Vapnik. 1995. The nature of Statistical Learning Theory. Springer-Verlag. [find](https://scholar.google.com/scholar?q=Vladimir+Vapnik%2E+1995%2E+The+nature+of+Statistical+Learning+Theory%2E+Springer%2DVerlag%2E)
17. Wei Wang, Kevin Knight, and Daniel Marcu. 2006. Capitalizing machine translation. In HLT-NAACL. [find](https://scholar.google.com/scholar?q=Wei+Wang%2C+Kevin+Knight%2C+and+Daniel+Marcu%2E+2006%2E+Capitalizing+machine+translation%2E+In+HLT%2DNAACL%2E)

*Updated 24 October 2026*