An important part of SMT is decoding - finding the best possible translation from source to target language. Very roughly, this procedure is following: Moses <http://www.statmt.org/moses/> generates a number of candidates (hypotheses) for each sentence/phrase in the training set, each of which is represented via a vector of features (e.g. 20) and has a BLEU score - roughly speaking, quality of translation.
An optimization algorithms uses this input to evolve a model, i.e. a vector of coefficients with dimensionality equal to the number of features. For example, for each phrase we want only one (best) translation. We assign the hypothesis with the highest BLEU score value 1 and values 0 to the remaining hypotheses. The objective function is the sum of squared differences between the true 'rank' (0 and 1) and the one evolved by the model. Currently the most popular optimizer is Minimum error rate training (MERT), see Bertoldi et al 'Improved MERT in Moses' (2009) and Och 'MERT in SMT' (2003). What I'm doing now is developing a GA to select the best candidate for each phrase. I expect GA to perform well on this task, as the database is relatively small, the full matrix is about 10^6 by 20, and GAs tend to perform well on classification-optimization problems, including ANNs. EDIT: I developed a real-coded evolutionary optimizer for solving this problem in C++. With a population=10, elitism=1, 1-bit-mutation and Laplace crossover with a = -1.1, b =1 (see Deep and Thakur, 2007) it evolves a vector of weights that give a squared error of around 90000, down from 293000 at the start of the run. The size of the database is ~1000 sentences with ~1000 hypotheses for each sentence. I would be grateful for any suggestions on how this result can be further improved, both in terms of other genetic operators (mutation/crossover/flip), as well as hybridization.A good link to a paper/report would do.
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