Paper: Unsupervised Dependency Parsing with Transferring Distribution via Parallel Guidance and Entropy Regularization

ACL ID P14-1126
Title Unsupervised Dependency Parsing with Transferring Distribution via Parallel Guidance and Entropy Regularization
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

We present a novel approach for induc- ing unsupervised dependency parsers for languages that have no labeled training data, but have translated text in a resource- rich language. We train probabilistic pars- ing models for resource-poor languages by transferring cross-lingual knowledge from resource-rich language with entropy reg- ularization. Our method can be used as a purely monolingual dependency parser, requiring no human translations for the test data, thus making it applicable to a wide range of resource-poor languages. We perform experiments on three Data sets ? Version 1.0 and version 2.0 of Google Universal Dependency Treebanks and Treebanks from CoNLL shared-tasks, across ten languages. We obtain state- of-the art performance of all the three data sets when compared with previo...