Cross-lingual transfer for unsupervised dependency parsing without parallel data

Long Duong, Trevor Cohn, Steven Bird, Paul Cook

Research output: Chapter in Book/Report/Conference proceedingConference Paper published in Proceedings

Abstract

Cross-lingual transfer has been shown to produce good results for dependency parsing of resource-poor languages. Although this avoids the need for a target language treebank, most approaches have still used large parallel corpora. However, parallel data is scarce for low-resource languages, and we report a new method that does not need parallel data. Our method learns syntactic word embeddings that generalise over the syntactic contexts of a bilingual vocabulary, and incorporates these into a neural network parser. We show empirical improvements over a baseline delexicalised parser on both the CoNLL and Universal Dependency Treebank datasets. We analyse the importance of the source languages, and show that combining multiple source-languages leads to a substantial improvement.

Original languageEnglish
Title of host publicationCoNLL 2015 - 19th Conference on Computational Natural Language Learning, Proceedings
PublisherAssociation for Computational Linguistics (ACL)
Pages113-122
Number of pages10
ISBN (Electronic)9781941643778
DOIs
Publication statusPublished - Jul 2015
Externally publishedYes
Event19th Conference on Computational Natural Language Learning, CoNLL 2015 - Beijing, China
Duration: 30 Jul 201531 Jul 2015

Publication series

NameCoNLL 2015 - 19th Conference on Computational Natural Language Learning, Proceedings

Conference

Conference19th Conference on Computational Natural Language Learning, CoNLL 2015
CountryChina
CityBeijing
Period30/07/1531/07/15

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  • Cite this

    Duong, L., Cohn, T., Bird, S., & Cook, P. (2015). Cross-lingual transfer for unsupervised dependency parsing without parallel data. In CoNLL 2015 - 19th Conference on Computational Natural Language Learning, Proceedings (pp. 113-122). (CoNLL 2015 - 19th Conference on Computational Natural Language Learning, Proceedings). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/K15-1012