A Modular Architecture for Separating Hypothesis Formation from Hypothesis Evaluation in Data-driven Machine Translation

Recent research in statisticaland examplebased machine translation integrates ruleinduced structured representations with statistics and lexicalised exceptions. While rulebased approaches concentrate on the formation of partial translation hypotheses, probabilistic approaches are concerned with the evaluation and selection of the best hypotheses. Within the METIS-II framework, we propose a machine translation system which uses transfer and expander rules to build an AND/OR graph of partial translations and a statistical ranker to find the best path through the graph. The paper gives an overview of the architecture and an evaluation of the system for several languages.