---
title: "Adaptation of the translation model for — Hildebrand 2005"
description: "The paper tackles domain mismatch in statistical machine translation."
authors: ["Almut Silja Hildebrand", "Matthias Eck", "Stephan Vogel", "Alex Waibel"]
year: 2005
venue: "Proceedings of the 10th EAMT Conference: Practical applications of machine translation"
source: "https://aclanthology.org/2005.eamt-1.19/"
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/papers/hildebrand-2005-adaptation-translation-model-statistical-machine/"
---
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# Adaptation of the translation model for statistical machine translation based on information retrieval

**Authors:** Almut Silja Hildebrand, Matthias Eck, [Stephan Vogel](https://md.research.elamur.ai/free/authors/stephan-vogel/), [Alex Waibel](https://md.research.elamur.ai/free/authors/alex-waibel/) · **Year:** 2005 · **Venue:** Proceedings of the 10th EAMT Conference: Practical applications of machine translation · **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/2005.eamt-1.19/) · [PDF](https://aclanthology.org/2005.eamt-1.19.pdf) · [OpenAlex](https://openalex.org/W2356613612)

**Citations:** 155 citations (openalex-snapshot · 2026-10-14) · [75](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper tackles domain mismatch in statistical machine translation. When large out-of-domain bilingual data is merged with a small in-domain corpus, the resulting translation model may pick word senses that are wrong for the test topic. The authors treat each source-side training sentence as a document and each test sentence as a query. They retrieve the most similar training sentences using TF-IDF cosine similarity and pool all retrieved pairs into one adapted corpus. They then retrain the translation model, and optionally the language model, on that corpus. To pick the selection size without a development set, they build language models on growing selections and choose the point where perplexity against the test sentence is lowest. They also re-rank retrieved batches by whether each batch lowers or raises perplexity. Experiments covered Spanish–English (medical) and Chinese–English (tourism). In both, adapted models beat strong baselines. On Chinese–English, perplexity-chosen sizes matched the best manually tuned results. This matters because it lets mixed or out-of-domain data help instead of hurt, with no separate in-domain tuning data needed.

## Key points
- Retrieval uses only the source side of the parallel data. No first-pass translation is needed, so selection does not depend on MT quality.
- The system combines IBM1 lexicon and phrase transducers with a trigram Kneser-Ney LM built with the SRI toolkit. Results are scored with NIST and BLEU.
- On Chinese–English, the in-domain baseline scored NIST 8.1129 / BLEU 0.4621. Perplexity-based size selection reached NIST 8.3812 / BLEU 0.4924. The best 3:1 in-/out-of-domain weighting gave NIST 8.3398 / BLEU 0.4931.
- Perplexity re-ranking for Chinese–English used batches of 20 sentences. Because of runtime limits, only the top 800 retrieved sentences were re-ranked in the first pass and the top 600 in the second. Re-ranking itself brought little gain there.
- Swapping TF-IDF for Okapi made no significant difference in translation quality. The two retrieval lists overlapped by more than 75% at the top 300 sentences and more than 90% at the top 1000.

## Interesting ideas
- **Test-set-driven data selection:** Use each test sentence as a retrieval query and pool the hits into one shared training set, rather than building one model per sentence. This keeps the model robust and could be applied to tailoring any model to a known target batch, such as domain-specific Ukrainian translation jobs.
- **Perplexity as a label-free stopping rule:** Train a small LM on the top-k retrieved data and choose k at the perplexity minimum on the target text. This removes the need for a reference-annotated dev set, which helps in low-resource settings.
- **Two-signal re-ranking:** Bag-of-words retrieval ignores word order, while LM perplexity captures it. The authors mark each batch as "good" or "bad" by its perplexity effect and move bad batches to the end, keeping the original order within each group. This cheaply merges the two signals and could improve corpus filtering pipelines.
- **Adapt the LM together with the TM:** Training the language model on the target side of the same selected data keeps it from overriding topic-appropriate translations. The general lesson is to keep all model components consistent when adapting to a domain.
- **Weighting in-domain versus retrieved data:** Duplicating or up-weighting the small in-domain portion (for example 3:1) noticeably shifted the scores. When mixing scarce clean data with large retrieved data, this ratio is worth tuning explicitly.

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

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/) · [Machine translation](https://md.research.elamur.ai/free/shelves/machine-translation/)

Topics: [Machine translation](https://md.research.elamur.ai/free/topics/machine-translation/)

## Cited by

Cited by

75 papers of our library cite it (of the 140673 whose bibliographies we read); 3 of them have a page here, 72 not yet.

- [Domain Adaptation for Statistical Machine Translation with Monolingual Resources](https://md.research.elamur.ai/free/papers/bertoldi-2009-domain-adaptation-statistical-machine-translation/)
- [Multi-Domain Neural Machine Translation through Unsupervised Adaptation](https://md.research.elamur.ai/free/papers/farajian-2017-multi-domain-neural-machine-translation/)
- [Measuring Machine Translation Errors in New Domains](https://md.research.elamur.ai/free/papers/irvine-2013-measuring-machine-translation-errors-new/)

## Cite

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

```bibtex
@inproceedings{hildebrand2005adaptation,
  title = {{Adaptation of the translation model for statistical machine translation based on information retrieval}},
  author = {Hildebrand, Almut Silja and Eck, Matthias and Vogel, Stephan and Waibel, Alex},
  booktitle = {Proceedings of the 10th EAMT Conference: Practical applications of machine translation},
  year = {2005},
  doi = {10.5445/ir/1000182997},
  url = {https://doi.org/10.5445/ir/1000182997}
}
```

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## References

15 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. BROWN, Peter E., DELLA PIETRA, Stephen A., DELLA PIETRA, Vincent J. and MERCER, Robert L. (1993). ‘The mathematics of statistical machine translation: Parameter estimation’, Computational Linguistics, 19(2), pp. 263-311 [find](https://scholar.google.com/scholar?q=BROWN%2C+Peter+E%2E%2C+DELLA+PIETRA%2C+Stephen+A%2E%2C+DELLA+PIETRA%2C+Vincent+J%2E+and+MERCER%2C+Robert+L%2E+%281993%29%2E+%E2%80%98The+mathematics+of+statistical+machine+translation%3A+Parameter+estimation%E2%80%99%2C+Computational+Linguistics%2C)
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3. ECK, Matthias, VOGEL, Stephan and WAIBEL, Alex (2004). ‘Language Model Adaptation for Statistical Machine Translation based on Information Retrieval’, Proceedings of LREC 2004, Lisbon, Portugal, May 2004. [find](https://scholar.google.com/scholar?q=ECK%2C+Matthias%2C+VOGEL%2C+Stephan+and+WAIBEL%2C+Alex+%282004%29%2E+%E2%80%98Language+Model+Adaptation+for+Statistical+Machine+Translation+based+on+Information+Retrieval%E2%80%99%2C+Proceedings+of+LREC+2004%2C+Lisbon%2C+Portugal%2C+May+2)
4. KIM, Woosung and KHUDANPUR, Sanjeev (2003). ‘Language Model Adaptation Using Cross-Lingual Information’, Proceedings of Eurospeech 2003, Geneva, Switzerland, Sept. 2003. [find](https://scholar.google.com/scholar?q=KIM%2C+Woosung+and+KHUDANPUR%2C+Sanjeev+%282003%29%2E+%E2%80%98Language+Model+Adaptation+Using+Cross%2DLingual+Information%E2%80%99%2C+Proceedings+of+Eurospeech+2003%2C+Geneva%2C+Switzerland%2C+Sept%2E+2003%2E)
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8. TAKEZAWA, Toshiyuki, SUMITA, Eiichiro, SU-GAYA, Fumiaki, YAMAMOTO, Hirofumi, YA-MAMOTO, Seiichi (2002) ‘Toward a Broad-coverage Bilingual Corpus for Speech Translation of Travel Conversation in the Real World’, LREC 2002 (Third International Conference on Language Resources and Evaluation), Vol.1, p… [find](https://scholar.google.com/scholar?q=TAKEZAWA%2C+Toshiyuki%2C+SUMITA%2C+Eiichiro%2C+SU%2DGAYA%2C+Fumiaki%2C+YAMAMOTO%2C+Hirofumi%2C+YA%2DMAMOTO%2C+Seiichi+%282002%29+%E2%80%98Toward+a+Broad%2Dcoverage+Bilingual+Corpus+for+Speech+Translation+of+Travel+Conversation+in+the+Re)
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12. New Orleans, LA. Sept. 2003. [find](https://scholar.google.com/scholar?q=New+Orleans%2C+LA%2E+Sept%2E+2003%2E)
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14. ZHANG, Ying, VOGEL, Stephan and WAIBEL, Alex (2003). ‘Integrated Phrase Segmentation and Alignment Algorithm for Statistical Machine Translation’, Proceedings of International Conference on Natural Language Processing and Knowledge Engineering 2003, Beijing, China, Oct. 2003. [find](https://scholar.google.com/scholar?q=ZHANG%2C+Ying%2C+VOGEL%2C+Stephan+and+WAIBEL%2C+Alex+%282003%29%2E+%E2%80%98Integrated+Phrase+Segmentation+and+Alignment+Algorithm+for+Statistical+Machine+Translation%E2%80%99%2C+Proceedings+of+International+Conference+on+Natural+La)
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