LATTICE-BASED MINIMUM ERROR RATE TRAINING USING WEIGHTED FINITE-STATE TRANSDUCERS WITH TROPICAL POLYNOMIAL WEIGHTS

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LATTICE-BASED MINIMUM ERROR RATE TRAINING USING WEIGHTED FINITE-STATE TRANSDUCERS WITH TROPICAL POLYNOMIAL WEIGHTS” by A. Waite, G. Blackwood, and W. Byrne. In Proceedings of the 10^th International Workshop on Finite State Methods and Natural Language Processing (FSMNLP 2012), (Donostia-San Sebastian, Spain), July 2012.

Abstract

Minimum Error Rate Training (MERT) is a method for training the parameters of a log-linear 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.

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BibTeX entry:

@inproceedings{fsmnlp12,
   author = {A. Waite and G. Blackwood and W. Byrne},
   title = {LATTICE-BASED MINIMUM ERROR RATE TRAINING USING WEIGHTED
	FINITE-STATE TRANSDUCERS WITH TROPICAL POLYNOMIAL WEIGHTS},
   booktitle = {Proceedings of the 10{\it ^{th}} International Workshop on
	Finite State Methods and Natural Language Processing (FSMNLP
	2012)},
   pages = {(11 pages)},
   address = {Donostia-San Sebastian, Spain},
   month = jul,
   year = {2012},
   url = {http://aclweb.org/anthology-new/W/W12/W12-6219.pdf}
}

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