Tree-to-tree Statistical Machine Translation models require the use of syntactic tree structures of both the source and target side in learning rules to guide the translation process. In order to accomplish the task, available treebanks for different languages are used as the main resources to collect necessary information to handle the translation task. However, since each treebank has its own defined tags, a barrier is inherently created in highlighting alignment relationships at different syntactic levels for different tag-sets. Moreover, these models are typically over constrained. This paper presents a unified tagset for all languages at Part-of-Speech and Phrasal Category level in tree-to-tree models. Different experiments are conducted to study for its feasibility, efficiency, and translation quality