Hierarchical system combination for machine translation

Given multiple translations of the same source sentence, how to combine them to produce a translation that is better than any single system output? We propose a hier-archical system combination framework for machine translation. This framework inte-grates multiple MT systems ’ output at the word-, phrase- and sentence- levels. By boosting common word and phrase trans-lation pairs, pruning unused phrases, and exploring decoding paths adopted by other MT systems, this framework achieves bet-ter translation quality with much less re-decoding time. The full sentence translation hypotheses from multiple systems are addi-tionally selected based on N-gram language models trained on word/word-POS mixed stream, which further improves the transla-tion quality. We consistently observed sig-nificant improvements on several test sets in multiple languages covering different gen-res.