---
title: "Cross-Language Parser Adaptation between — Zeman 2008"
description: "The paper asks whether a usable parser can be built for a resource-poor language without first creating a treebank for it."
authors: ["Daniel Zeman", "Philip Resnik"]
year: 2008
venue: "Proceedings of the IJCNLP-08 Workshop on NLP for Less Privileged Languages"
source: "https://aclanthology.org/I08-3008/"
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/zeman-2008-cross-language-parser-adaptation-between/"
---
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# Cross-Language Parser Adaptation between Related Languages

**Authors:** [Daniel Zeman](https://md.research.elamur.ai/free/en/authors/daniel-zeman/), [Philip Resnik](https://md.research.elamur.ai/free/en/authors/philip-resnik/) · **Year:** 2008 · **Venue:** Proceedings of the IJCNLP-08 Workshop on NLP for Less Privileged Languages · **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/I08-3008/) · [PDF](https://aclanthology.org/I08-3008.pdf) · [OpenAlex](https://openalex.org/W2144571677)

**Citations:** 156 citations (openalex-snapshot · 2026-10-14) · [126](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper asks whether a usable parser can be built for a resource-poor language without first creating a treebank for it. The authors borrow from McClosky et al.'s domain adaptation work and treat two closely related languages as two "domains" of one language. Danish serves as the source and Swedish as the target. They train the Charniak-Johnson reranking parser on the Danish Dependency Treebank, which they convert to phrase structures, and evaluate it on Talbanken05. To make the comparison fair, they first normalize the annotation styles of the two treebanks and map their tag sets onto each other. Two techniques address the vocabulary mismatch: glosses, which use a Danish-Swedish bitext, and delexicalization, which parses over tags instead of words. They also try self-training on unlabeled Swedish text from JRC-Acquis. The best system, a delexicalized reranking parser, reaches 66.40% labeled F-score against a plain baseline of about 43%. That matches a parser trained on roughly 1,500 native Swedish trees. Building a treebank that size would take months, while this approach could be set up in days.

## Key points
- **Data:**
  - Danish Dependency Treebank (CoNLL 2006 version): 5,190 sentences, split into 4,900 for training and 290 for testing (276 of them ≤40 words).
  - Talbanken05: 11,042 sentences, split into 10,700 for training and 342 for testing (317 of them ≤40 words).
  - Unlabeled data from JRC-Acquis: 430,808 Swedish sentences (about 6.15M tokens), plus 283,509 aligned Danish-Swedish segments, word-aligned with GIZA++.
- **Pipeline:**
  - Dependencies are converted to constituents with the "flattest-possible" algorithm.
  - Head morphological tags are mapped to Penn Treebank-style non-terminals, so the Charniak-Johnson parser can run unmodified.
  - Swedish text was retagged with a Swedish version of Hajič's tagger.
- **Main result:**
  - The delexicalized Brown (reranking) parser scored 66.40% F.
  - This is about 23 points absolute above the baseline, or roughly 41% error reduction.
  - On the Swedish learning curve, it equals about 1,546 training trees.
- **Self-training:**
  - Swedish Acquis was parsed and added to the training data, without retraining the reranker.
  - This did not beat the best Part One result, and the Danish-trained reranker gave no gain in this setting.
- **Upper bound:** A parser trained on delexicalized, automatically tagged Swedish (hybrid tags) reaches 77.01% F. Better treebank and tag mapping and better tagging are named as ways to close the gap.

## Interesting ideas
- **Related languages as domains:** Domain-adaptation machinery such as reranking and self-training can be reused across closely related languages. This could transfer to other pairs with shared syntax but different spelling.
- **Normalizing annotation conventions first:** Before measuring cross-language transfer, the authors harmonize systematic style differences. Examples are how coordination, determiners, numerals and genitives attach. This keeps the result from mixing language effects with guideline effects. It is a useful step whenever treebanks from different teams are combined.
- **Interlingua-style tag conversion:** Each tag set gets a driver that decodes tags into a shared feature space and encodes them back.
  - Feature priorities resolve conflicts; for example, part of speech outranks gender.
  - Drivers are written once and reused for any pair of tag sets, a design that UD-style feature schemes echo.
- **Delexicalization with restuffing:** The parser runs on tags only, and the original words are put back into the output afterwards. The restored, word-level parses can then train a lexicalized model in the target language.
- **Treebank-size-equivalent evaluation:** The authors place the adapted parser's score on the target-language learning curve to see how many annotated trees it is worth. This turns an abstract F-score into a concrete annotation cost, which helps when deciding whether to fund a new treebank.

*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: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

Topics: [Treebanks](https://md.research.elamur.ai/free/en/topics/treebanks/) · [Part-of-speech tagging](https://md.research.elamur.ai/free/en/topics/part-of-speech-tagging/)

## Cited by

Cited by

126 papers of our library cite it (of the 140884 whose bibliographies we read); 20 of them have a page here, 106 not yet.

- [Multilingual Projection for Parsing Truly Low-Resource Languages](https://md.research.elamur.ai/free/en/papers/agic-2016-multilingual-projection-parsing-truly-low/)
- [Cross-Lingual Parser Selection for Low-Resource Languages](https://md.research.elamur.ai/free/en/papers/agic-2017-cross-lingual-parser-selection-low/)
- [Many Languages, One Parser](https://md.research.elamur.ai/free/en/papers/ammar-2016-many-languages-one-parser/)
- [Massively Multilingual Word Embeddings](https://md.research.elamur.ai/free/en/papers/ammar-2016-massively-multilingual-word-embeddings/)
- [A Falta de Pan, Buenas Son Tortas: The Efficacy of Predicted UPOS Tags for Low Resource UD Parsing](https://md.research.elamur.ai/free/en/papers/anderson-2021-falta-de-pan-buenas-son/)
- [Parsing in the absence of related languages: Evaluating low-resource dependency parsers on Tagalog](https://md.research.elamur.ai/free/en/papers/aquino-2020-parsing-absence-related-languages-evaluating/)
- [Cross-lingual and Supervised Models for Morphosyntactic Annotation: a Comparison on Romanian](https://md.research.elamur.ai/free/en/papers/aufrant-2016-cross-lingual-supervised-models-morphosyntactic/)
- [Transferring Coreference Resolvers with Posterior Regularization](https://md.research.elamur.ai/free/en/papers/martins-2015-transferring-coreference-resolvers-posterior-regularization/)
- [Multi-Source Transfer of Delexicalized Dependency Parsers](https://md.research.elamur.ai/free/en/papers/mcdonald-2011-multi-source-transfer-delexicalized-dependency/)
- [Selective Sharing for Multilingual Dependency Parsing](https://md.research.elamur.ai/free/en/papers/naseem-2012-selective-sharing-multilingual-dependency-parsing/)
- [Learning part-of-speech taggers with inter-annotator agreement loss](https://md.research.elamur.ai/free/en/papers/plank-2014-learning-part-speech-taggers-inter/)
- [Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing](https://md.research.elamur.ai/free/en/papers/ponti-2019-modeling-language-variation-universals-survey/)
- [The Persian Dependency Treebank Made Universal](https://md.research.elamur.ai/free/en/papers/safari-2022-persian-dependency-treebank-made-universal/)
- [Cross-lingual Transfer Learning for Multilingual Task Oriented Dialog](https://md.research.elamur.ai/free/en/papers/schuster-2019-cross-lingual-transfer-learning-multilingual/)
- [Diachronic proximity vs. data sparsity in cross-lingual parser projection. A case study on Germanic](https://md.research.elamur.ai/free/en/papers/sukhareva-2014-diachronic-proximity-vs-data-sparsity/)
- [Cross-lingual Word Clusters for Direct Transfer of Linguistic Structure](https://md.research.elamur.ai/free/en/papers/tackstrom-2012-cross-lingual-word-clusters-direct/)
- [Cross-Lingual Dependency Parsing with Universal Dependencies and Predicted PoS Labels](https://md.research.elamur.ai/free/en/papers/tiedemann-2015-cross-lingual-dependency-parsing-universal/)
- [Universal Dependencies v1: A Multilingual Treebank Collection](https://md.research.elamur.ai/free/en/papers/ud-2016-nivre-universal-dependencies-v1-multilingual-treebank/)
- ['IMS at the CoNLL 2017 UD Shared Task: CRFs and Perceptrons Meet Neural Networks'](https://md.research.elamur.ai/free/en/papers/ud-2017-bjorkelund-ims-conll-2017-ud-shared/)
- [Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT](https://md.research.elamur.ai/free/en/papers/wu-2019-beto-bentz-becas-surprising-cross/)

## Cite

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

```bibtex
@inproceedings{zeman2008cross,
  title = {{Cross-Language Parser Adaptation between Related Languages}},
  author = {Zeman, Daniel and Resnik, Philip},
  booktitle = {Proceedings of the IJCNLP-08 Workshop on NLP for Less Privileged Languages},
  year = {2008},
  url = {https://aclanthology.org/I08-3008/}
}
```

- [← previous Coreference-inspired Coherence Modeling](https://md.research.elamur.ai/free/en/papers/elsner-2008-coreference-inspired-coherence-modeling/)
- [next → Dependency-based Semantic Role Labeling of PropBank](https://md.research.elamur.ai/free/en/papers/johansson-2008-dependency-based-semantic-role-labeling/)
- [↑ back to papers of 2008](https://md.research.elamur.ai/free/en/papers/2008/)

## References

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

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3. Alena Böhmová, Jan Hajič, Eva Hajičová, Barbora Hladká. 2003. The Prague Dependency Treebank: A Three-Level Annotation Scenario. In: Anne Abeillé (ed.): Treebanks: Building and Using Syntactically Annotated Corpora. Kluwer Academic Publishers, Dordrecht, The Netherlands. [find](https://scholar.google.com/scholar?q=Alena+B%C3%B6hmov%C3%A1%2C+Jan+Haji%C4%8D%2C+Eva+Haji%C4%8Dov%C3%A1%2C+Barbora+Hladk%C3%A1%2E+2003%2E+The+Prague+Dependency+Treebank%3A+A+Three%2DLevel+Annotation+Scenario%2E+In%3A+Anne+Abeill%C3%A9+%28ed%2E%29%3A+Treebanks%3A+Building+and+Using+Syntactically+Ann)
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8. Rebecca Hwa, Philip Resnik, Amy Weinberg, Clara Cabezas, Okan Kolak. 2004. Bootstrapping Parsers via Syntactic Projection across Parallel Texts. In: Natural Language Engineering 1 (1): 1–15. Cambridge University Press, Cambridge, England. [find](https://scholar.google.com/scholar?q=Rebecca+Hwa%2C+Philip+Resnik%2C+Amy+Weinberg%2C+Clara+Cabezas%2C+Okan+Kolak%2E+2004%2E+Bootstrapping+Parsers+via+Syntactic+Projection+across+Parallel+Texts%2E+In%3A+Natural+Language+Engineering+1+%281%29%3A+1%E2%80%9315%2E+Cambridge)
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10. Philipp Koehn, Franz Josef Och, Daniel Marcu. 2003. Statistical Phrase-Based Translation. In: Proceedings of HLT-NAACL 2003, pp. 127–133. Edmonton, Canada. [find](https://scholar.google.com/scholar?q=Philipp+Koehn%2C+Franz+Josef+Och%2C+Daniel+Marcu%2E+2003%2E+Statistical+Phrase%2DBased+Translation%2E+In%3A+Proceedings+of+HLT%2DNAACL+2003%2C+pp%2E+127%E2%80%93133%2E+Edmonton%2C+Canada%2E)
11. Matthias T. Kromann, Line Mikkelsen, Stine Kern Lynge. 2004. Danish Dependency Treebank. At: [http://www.id.cbs.dk/~mtk/treebank/](http://www.id.cbs.dk/~mtk/treebank/). København, Denmark. [↗](http://www.id.cbs.dk/~mtk/treebank/)
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