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In current phrase-based SMT systems, more training data is generally better than less. However, a larger data set eventually introduces a larger model that enlarges the search space for the translation problem, and consequently requires more time and more resources to translate. We argue redundant information in a SMT system may not only delay the computations but also affect the quality of the outputs. This paper proposes an approach to reduce the model size by filtering out the less probable entries based on compatible data in an intermediate language, a novel use of triangulation, without sacrificing the translation quality. Comprehensive experiments were conducted on standard data sets. We achieved significant quality improvements (up to 2.3 Bleu points) while translating with reduced models. In addition, we demonstrate a straightforward combination method for more progressive filtering. The reduction of the model size can be up to 94% with the translation quality being preserved.

Intersecting multilingual data for faster and better statistical translations