We take a multi-pass approach to machine translation decoding when using synchronous context-free grammars as the translation model and n-gram language models: the first pass uses a bigram language model, and the resulting parse forest is used in the second pass to guide search with a trigram language model. The trigram pass closes most of the performance gap between a bigram decoder and a much slower trigram decoder, but takes time that is insignificant in comparison to the bigram pass. An additional fast decoding pass maximizing the expected count of correct translation hypotheses increases the BLEU score significantly. .
