Using a dependency parser to improve SMT for subject-object-verb languages
Peng Xu, Jaeho Kang, Michael Ringgaard, Franz Josef Och · 2009
We introduce a novel precedence reordering approach based on a dependency parser to statistical machine translation systems.Similar to other preprocessing reordering approaches, our method can efficiently incorporate linguistic knowledge into SMT systems without increasing the complexity of decoding.For a set of five subject-object-verb (SOV) order languages, we show significant improvements in BLEU scores when translating from English, compared to other reordering approaches, in state-of-the-art phrase-based SMT systems.