Facing the most difficult case of Semantic Role Labeling: A collaboration of word embeddings and co training
We present a successful collaboration of word embeddings and co training to tackle in the most difficult test case of semantic role labeling: predicting out of domain and unseen semantic frames. Despite the fact that co training is a successful traditional semi supervised method, its application in SRL is very limited especially when a huge amount of labeled data is available. In this work, co training is used together with word embeddings to improve the performance of a system trained on a large training dataset. We also introduce a semantic role labeling system with a simple learning architecture and effective inference that is easily adaptable to semi supervised settings with new training data and/or new features. On the out of domain testing set of the standard benchmark CoNLL 2009 data our simple approach achieves high performance and improves state of the art results.
COLING