Stochastic inversion transduction grammars and bilingual parsing of parallel corpora
Computational Linguistics
Bootstrapping parsers via syntactic projection across parallel texts
Natural Language Engineering
Experiments in parallel-text based grammar induction
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Corpus-based induction of syntactic structure: models of dependency and constituency
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
A backoff model for bootstrapping resources for non-English languages
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
CoNLL-X shared task on multilingual dependency parsing
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Two languages are better than one (for syntactic parsing)
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Unsupervised multilingual grammar induction
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Phylogenetic grammar induction
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Using universal linguistic knowledge to guide grammar induction
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Data point selection for cross-language adaptation of dependency parsers
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Unsupervised structure prediction with non-parallel multilingual guidance
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Multi-source transfer of delexicalized dependency parsers
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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We present a novel algorithm for multilingual dependency parsing that uses annotations from a diverse set of source languages to parse a new unannotated language. Our motivation is to broaden the advantages of multilingual learning to languages that exhibit significant differences from existing resource-rich languages. The algorithm learns which aspects of the source languages are relevant for the target language and ties model parameters accordingly. The model factorizes the process of generating a dependency tree into two steps: selection of syntactic dependents and their ordering. Being largely language-universal, the selection component is learned in a supervised fashion from all the training languages. In contrast, the ordering decisions are only influenced by languages with similar properties. We systematically model this cross-lingual sharing using typological features. In our experiments, the model consistently outperforms a state-of-the-art multi-lingual parser. The largest improvement is achieved on the non Indo-European languages yielding a gain of 14.4%.