Reordering experiments for n-gram-based SMT

This paper addresses the problem of reordering in statistical machine translation (SMT). We describe an elegant and efficient approach to couple reordering (word order monotonization) and decoding, which does not need for any additional model. We use linguistically motivated reordering rules to extend a monotonic search graph (with reordering hypotheses). The extended graph is traversed in decoding when a fully- informed decision can be taken (no preprocessing decision about reordering is taken). We also show how the N-gram translation model can be successfully used as reordering model when estimated with reordered source words (to harmonize the source and target word order). Experiments are reported on the Euparl task (Spanish- to-English and English-to-Spanish). Results are presented regarding translation accuracy and computational efficiency, showing significant improvements in translation quality for both translation directions at a very low computational cost.