Chunk-Based Verb Reordering in VSO Sentences for Arabic-English Statistical Machine Translation

In Arabic-to-English phrase-based statistical 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 detect and displace clause-initial verbs in the Arabic side of a word-aligned parallel corpus. This method is applied to preprocess the training data, and to collect statistics about verb movements. From this analysis, specific verb reordering lattices are then built on the test sentences before decoding them. The application of our reordering methods on the training and test sets results in consistent BLEU score improvements on the NIST-MT 2009 Arabic-English benchmark.

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