Cross-lingual Model Transfer Using Feature Representation Projection

Mikhail Kozhevnikov, Ivan Titov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

We propose a novel approach to crosslingual model transfer based on feature
representation projection. First, a compact feature representation relevant for the task in question is constructed for either language independently and then the mapping between the two representations is determined using parallel data. The target instance can then be mapped into the source-side feature representation using the derived mapping and handled directly by the source-side model. This approach displays competitive performance on model transfer for semantic role labeling when compared to direct model transfer and annotation projection and suggests interesting directions for further research.
Original languageEnglish
Title of host publicationProceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014, June 22-27, 2014, Baltimore, MD, USA, Volume 2: Short Papers
PublisherAssociation for Computational Linguistics
Number of pages7
Publication statusPublished - Jun 2014


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