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Publication
FSMNLP 2012
Conference paper
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.