The present article reports on efforts to improve the translation accuracy of a corpus-based hybrid MT system developed using the PRESEMT methodology. This methodology operates on a phrasal basis, where phrases are linguistically-motivated but are automatically determined via a dedicated module. Here, emphasis is placed on improving the structure of each translated sentence, by replacing the Example-Based MT approach originally used in PRESEMT with a sub-sentential approach. Results indicate that an improved accuracy can be achieved, as measured by objective metrics.