Compositions of Tree-to-Tree Statistical Machine Translation Models

Abstract

The well-known synchronous context-free grammars (SCFGs) and synchronous tree-substitution grammars (STSGs), both of which are used as tree-to-tree translation models in statistical machine translation are investigated. Their composition hierarchies are established in both the unweighted as well as the weighted case. More precisely, it is shown that SCFGs are closed under composition in both cases and that there is a close connection between compositions of STSGs and compositions of certain tree transducers. With the help of the close ties, the composition closure of STSGs is identified in both cases as well. The results for the weighted case utilize a new lifting technique that might prove useful also in similar setups.