Scaling up Automatic Cross-Lingual Semantic Role Annotation

Lonneke van der Plas,  Paola Merlo,  James Henderson
University of Geneva


Abstract

Broad-coverage semantic annotations for training statistical learners are only available for a handful of languages. Previous approaches to cross-lingual transfer of semantic annotations have addressed this problem with encouraging results on a small scale. In this paper, we scale up previous efforts by using an automatic approach to semantic annotation that does not rely on a semantic ontology for the target language. Moreover, we improve the quality of the transferred semantic annotations by using a joint syntactic-semantic parser that learns the correlations between syntax and semantics of the target language and smooths out the errors from automatic transfer. We reach a labelled F-measure for predicates and arguments of only 4% and 9% points, respectively, lower than the upper bound from manual annotations.




Full paper: http://www.aclweb.org/anthology/P/P11/P11-2052.pdf