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EDA: AN EVOLUTIONARY DECODING ALGORITHM FOR STATISTICAL MACHINE TRANSLATION

In a statistical machine translation system (SMTS), decoding is the process of finding the most likely translation based on a statistical model, according to previously learned parameters. The success of an SMTS is strongly dependent on the quality of its decoder. Most of the SMTS's published in current literature use approaches based on traditional optimization methods and heuristics. On the other hand, over the last few years there has been a rapid increase in the use of metaheuristics. These kinds of techniques have shown to be able to solve difficult search problems in an efficient way for a wide number of applications. This paper proposes a new approach based on evolutionary hybrid algorithms to translate sentences in a specific technical context. The algorithm has been enhanced by adaptive parameter control. The tests are carried out in the context of Spanish and then translated to English. The experimental results validate the superior performance of our method in contrast to a statistical greedy decoder. We also compare our new approach to the existing public domain general translators.