Abstract
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. This paper proposes a new approach based on evolutionary hybrid algorithms to translate sentences in a specific technical context. The tests are carried out in the context of Spanish and then translated to English. The experimental results validate the performance of our method.
This research was supported by Fondecyt Project 1040364
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Otto, E., Riff, M.C. (2004). Towards an Efficient Evolutionary Decoding Algorithm for Statistical Machine Translation. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds) MICAI 2004: Advances in Artificial Intelligence. MICAI 2004. Lecture Notes in Computer Science(), vol 2972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24694-7_45
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DOI: https://doi.org/10.1007/978-3-540-24694-7_45
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