Novel Reordering Approaches in Phrase-Based Statistical MachineTranslation

摘要

This paper presents novel approaches toreordering in phrase-based statistical machinetranslation. We perform consistentreordering of source sentences in trainingand estimate a statistical translationmodel. Using this model, we follow aphrase-based monotonic machine translationapproach, for which we develop an efficientand flexible reordering frameworkthat allows to easily introduce different reorderingconstraints. In translation, weapply source sentence reordering on wordlevel and use a reordering automaton as input.We show how to compute reorderingautomata on-demand using IBM or ITGconstraints, and also introduce two newtypes of reordering constraints. We furtheradd weights to the reordering automata.We present detailed experimental resultsand show that reordering significantly improvestranslation quality.