---
title: "Cross-lingual Dependency Parsing Based on — Guo 2015"
description: "The paper tackles parser induction for languages with little or no annotated syntax."
authors: ["Jiang Guo", "Wanxiang Che", "David Yarowsky", "Haifeng Wang", "Ting Liu"]
year: 2015
venue: "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)"
source: "https://aclanthology.org/P15-1119/"
doi: "10.3115/v1/p15-1119"
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/guo-2015-cross-lingual-dependency-parsing-based/"
---
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# Cross-lingual Dependency Parsing Based on Distributed Representations

**Authors:** [Jiang Guo](https://md.research.elamur.ai/free/en/authors/jiang-guo/), [Wanxiang Che](https://md.research.elamur.ai/free/en/authors/wanxiang-che/), [David Yarowsky](https://md.research.elamur.ai/free/en/authors/david-yarowsky/), [Haifeng Wang](https://md.research.elamur.ai/free/en/authors/haifeng-wang/), [Ting Liu](https://md.research.elamur.ai/free/en/authors/ting-liu/) · **Year:** 2015 · **Venue:** Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · **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/P15-1119/) · [PDF](https://aclanthology.org/P15-1119.pdf) · [DOI 10.3115/v1/p15-1119](https://doi.org/10.3115/v1/p15-1119) · [OpenAlex](https://openalex.org/W2250741688)

**Citations:** 180 citations (openalex-snapshot · 2026-10-14) · [89](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper tackles parser induction for languages with little or no annotated syntax. A parser is trained only on an English treebank and then applied to other languages. Earlier model-transfer work relied on delexicalized parsers that discard word identity, which especially hurts labeled parsing. The authors restore lexical information by placing words from both languages in one shared embedding space.

They build on the Chen and Manning (2014) greedy arc-standard neural parser with its cube activation, and add distance, valency and cross-lingual word cluster features. Two methods produce the bilingual word vectors:
- **Robust projection (PROJ)**
- **CCA-based alignment**

Neither method needs parallel text; each requires only a bilingual lexicon.

Training uses the English universal multilingual treebanks (McDonald et al., 2013), with German, Spanish and French as targets. The full system lowers LAS error by 10.9% on average relative to the delexicalized baseline. It also beats McDonald et al. (2013) with projected cluster features. This matters because many low-resource languages lack the bitexts that other transfer approaches depend on.

## Key points
- Data: McDonald et al.'s (2013) universal multilingual treebank collection, transferring from English (EN) to German (DE), Spanish (ES) and French (FR). Metrics are UAS and LAS.
- The combined system (embeddings plus clusters) gives an average 10.9% relative LAS error reduction over the delexicalized parser.
- In the comparison of bilingual embeddings, PROJ achieves the best LAS: 49.94 (DE), 61.76 (ES) and 61.55 (FR). CCA scores 49.32, 59.65 and 59.50, while MTL, BIAE, BICVM and BILBOWA score lower.
- Some competing embeddings cover only about 31% of words in the DE test treebank, compared with over 70% for CCA and PROJ.
- The code is released at github.com/jiangfeng1124/acl15-clnndep.

*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: [Dependency parsing](https://md.research.elamur.ai/free/en/topics/dependency-parsing/) · [Treebanks](https://md.research.elamur.ai/free/en/topics/treebanks/)

## Cited by

Cited by

89 papers of our library cite it (of the 140884 whose bibliographies we read); 9 of them have a page here, 80 not yet.

- [Adversarial Multitask Learning for Joint Multi-Feature and Multi-Dialect Morphological Modeling](https://md.research.elamur.ai/free/en/papers/acl-2019-zalmout-adversarial-multitask-learning-joint-multi/)
- [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/)
- [Cross-lingual RST Discourse Parsing](https://md.research.elamur.ai/free/en/papers/braud-2017-cross-lingual-rst-discourse-parsing/)
- [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/)
- [Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing](https://md.research.elamur.ai/free/en/papers/schuster-2019-cross-lingual-alignment-contextual-word/)
- [The Galactic Dependencies Treebanks: Getting More Data by Synthesizing New Languages](https://md.research.elamur.ai/free/en/papers/ud-2016-wang-galactic-dependencies-treebanks-getting-more/)
- [Universal Dependencies Parsing for Colloquial Singaporean English](https://md.research.elamur.ai/free/en/papers/ud-2017-wang-universal-dependencies-parsing-colloquial-singaporean/)
- [Parsing Tweets into Universal Dependencies](https://md.research.elamur.ai/free/en/papers/ud-2018-liu-parsing-tweets-into-universal-dependencies/)

## Cite

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

```bibtex
@inproceedings{guo2015cross,
  title = {{Cross-lingual Dependency Parsing Based on Distributed Representations}},
  author = {Guo, Jiang and Che, Wanxiang and Yarowsky, David and Wang, Haifeng and Liu, Ting},
  booktitle = {Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  year = {2015},
  doi = {10.3115/v1/p15-1119},
  url = {https://doi.org/10.3115/v1/p15-1119}
}
```

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## References

56 references, 19 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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