Lattice-based minimum error rate training using weighted finite-state transducers with tropical polynomial weights

Abstract

Minimum Error Rate Training (MERT) is a method for training the parameters of a loglinear model. One advantage of this method of training is that it can use the large number of hypotheses encoded in a translation lattice as training data. We demonstrate that the MERT line optimisation can be modelled as computing the shortest distance in a weighted finite-state transducer using a tropical polynomial semiring.