Self-training should also benefit other discriminatively trained parsers with latent annotations ( Petrov and Klein , 2008 ) , although training would be much slower compared to using generative models , as in our case .
sparse multiscale grammars for discriminative latent variable parsing
Slav Petrov Dan Klein
Better results would be expected by combining the PCFG-LA parser with discriminative reranking approaches ( Charniak and Johnson , 2005 ; Huang , 2008 ) for self training . Self-training should also benefit other discriminatively trained parsers with latent annotations ( Petrov and Klein , 2008 ) , although training would be much slower compared to using generative models , as in our case . In future work , we plan to scale up the training process with more unlabeled training data ( e.g. , gigaword ) and investigate automatic selection of materials that are most suitable for self-training .
Self-training should also benefit other discriminatively trained parsers with latent annotations ( @@CITATION ) , although training would be much slower compared to using generative models , as in our case .