A Bayesian Model of Multilingual Unsupervised Semantic Role Induction
Table 2: Results for the Multilingual Model with using labeled data for the source language.
['[EMPTY]', '[BOLD] Source', '[BOLD] Target', '[BOLD] English  [BOLD] PU', '[BOLD] English  [BOLD] CO', '[BOLD] English  [BOLD] F1', '[BOLD] German  [BOLD] PU', '[BOLD] German  [BOLD] CO', '[BOLD] German  [BOLD] F1']
Another motivation for jointly modeling SRL in multiple languages is the transfer of information from a resource rich language to a resource poor language. We evaluated our model in a very general annotation transfer scenario, where we have a small labeled dataset for one language (source), and a large parallel unlabeled dataset for the source and another (target) language. We investigate whether this setting improves the parameter estimates for the target language. This data gives us good parameters for the source language, which are used to sample the roles of the source language in the unlabeled Europarl data. The CLVs aim to capture this improvement and thereby improve sampling and parameter estimates for the target language. We obtain small improvements in the target languages. As in the unsupervised setting, the small percentage of aligned roles probably limits the impact of the cross-lingual information.