Lexicalized reordering in multiple-graph based statistical machine translation

Reordering is one of the key problems in statistical machine translation (SMT). This paper first presents how lexicalized reordering is embedded into a phrase-based SMT framework modeled by multiple-graph that was formulated in our previous work. Specifically, we show how lazy reordering graph is computed and combined with our previously proposed multiple layer search (MLS) to achieve an efficient reordering decoding that is also flexible to take various reordering models. Secondly, we introduce a variety of lexicalized reordering models employed in our system that significantly improved system performance.