Paper: Training Factored PCFGs with Expectation Propagation

ACL ID D12-1105
Title Training Factored PCFGs with Expectation Propagation
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2012

PCFGs can grow exponentially as additional annotations are added to an initially simple base grammar. We present an approach where multiple annotations coexist, but in a factored manner that avoids this combinatorial explo- sion. Our method works with linguistically- motivated annotations, induced latent struc- ture, lexicalization, or any mix of the three. We use a structured expectation propagation algorithm that makes use of the factored struc- ture in two ways. First, by partitioning the fac- tors, it speeds up parsing exponentially over the unfactored approach. Second, it minimizes the redundancy of the factors during training, improving accuracy over an independent ap- proach. Using purely latent variable annota- tions, we can efficiently train and parse with up to 8 latent bits per ...