Chunk-Based Verb Reordering in VSO Sentences for Arabic-English Statistical Machine Translation
Arianna Bisazza, Marcello Federico · 2010
In Arabic-to-English phrase-based statis-tical machine translation, a large number of syntactic disfluencies are due to wrong long-range reordering of the verb in VSO sentences, where the verb is anticipated with respect to the English word order. In this paper, we propose a chunk-based reordering technique to automatically de-tect and displace clause-initial verbs in the Arabic side of a word-aligned parallel cor-pus. This method is applied to preprocess the training data, and to collect statistics about verb movements. From this anal-ysis, specific verb reordering lattices are then built on the test sentences before de-coding them. The application of our re-ordering methods on the training and test sets results in consistent BLEU score im-provements on the NIST-MT 2009 Arabic-English benchmark. 1