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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.

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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