Pre-ordering of phrase-based machine translation input in translation workflow
Alexandru Ceauşu, Sabine Hunsicker · 2014
Word reordering is a difficult task for decoders when the languages involved have a significant difference in syntax.Phrase-based statistical machine translation (PBSMT), preferred in commercial settings due to its maturity, is particularly prone to errors in long range reordering.Source sentence pre-ordering, as a pre-processing step before PBSMT, proved to be an efficient solution that can be achieved using limited resources.We propose a dependency-based pre-ordering model with parameters optimized using a reordering score to pre-order the source sentence.The source sentence is then translated using an existing phrase-based system.The proposed solution is very simple to implement.It uses a hierarchical phrase-based statistical machine translation system (HPBSMT) for pre-ordering, combined with a PBSMT system for the actual translation.We show that the system can provide alternate translations of less post-editing effort in a translation workflow with German as the source language.