Bayesian Inference for Finite-State Transducers
David Chiang, Jonathan Graehl, Kevin K. Knight, Adam Pauls, Sujith Ravi · 2010
We describe a Bayesian inference algorithm that can be used to train any cascade of weighted finite-state transducers on end-toend data. We also investigate the problem of automatically selecting from among multiple training runs. Our experiments on four different tasks demonstrate the genericity of this framework, and, where applicable, large improvements in performance over EM. We also show, for unsupervised part-of-speech tagging, that automatic run selection gives a large improvement over previous Bayesian approaches. 1