Hierarchical system combination for machine translation

Abstract

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 hierarchical system combination framework for machine translation. This framework integrates multiple MT systems' output at the word-, phrase- and sentence- levels. By boosting common word and phrase translation pairs, pruning unused phrases, and exploring decoding paths adopted by other MT systems, this framework achieves better translation quality with much less redecoding time. The full sentence translation hypotheses from multiple systems are additionally selected based on N-gram language models trained on word/word-POS mixed stream, which further improves the translation quality. We consistently observed significant improvements on several test sets in multiple languages covering different genres. © 2007 Association for Computational Linguistics.