Paper: Experiments with Spectral Learning of Latent-Variable PCFGs

ACL ID N13-1015
Title Experiments with Spectral Learning of Latent-Variable PCFGs
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2013
Authors

Latent-variable PCFGs (L-PCFGs) are a highly successful model for natural language parsing. Recent work (Cohen et al., 2012) has introduced a spectral algorithm for param- eter estimation of L-PCFGs, which?unlike the EM algorithm?is guaranteed to give con- sistent parameter estimates (it has PAC-style guarantees of sample complexity). This paper describes experiments using the spectral algo- rithm. We show that the algorithm provides models with the same accuracy as EM, but is an order of magnitude more efficient. We de- scribe a number of key steps used to obtain this level of performance; these should be rel- evant to other work on the application of spec- tral learning algorithms. We view our results as strong empirical evidence for the viability of spectral methods as an alternative to...