Expanding the Language model in a low-resource hybrid MT system
George Tambouratzis, Sokratis Sofianopoulos, Marina Vassiliou · 2014
The present article investigates the fusion of different language models to improve translation accuracy.A hybrid MT system, recentlydeveloped in the European Commissionfunded PRESEMT project that combines example-based MT and Statistical MT principles is used as a starting point.In this article, the syntactically-defined phrasal language models (NPs, VPs etc.) used by this MT system are supplemented by n-gram language models to improve translation accuracy.For specific structural patterns, n-gram statistics are consulted to determine whether the pattern instantiations are corroborated.Experiments indicate improvements in translation accuracy.