Paper: Bootstrapping Semantic Role Labelers from Parallel Data

ACL ID S13-1044
Title Bootstrapping Semantic Role Labelers from Parallel Data
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2013
Authors

We present an approach which uses the sim- ilarity in semantic structure of bilingual par- allel sentences to bootstrap a pair of seman- tic role labeling (SRL) models. The setting is similar to co-training, except for the inter- mediate model required to convert the SRL structure between the two annotation schemes used for different languages. Our approach can facilitate the construction of SRL models for resource-poor languages, while preserving the annotation schemes designed for the tar- get language and making use of the limited re- sources available for it. We evaluate the model on four language pairs, English vs German, Spanish, Czech and Chinese. Consistent im- provements are observed over the self-training baseline.