关键词: 统计机器翻译, 预调序模型, N-best句法树, 调序规则, 规则优化

Abstract: The syntactic heterogeneity between source and target languages has a significant impact on Statistical Machine Translation（SMT） performance. Based on the Chinese-English SMT system, an N-best syntactic knowledge enhanced method is proposed to pre-order the source-side sentences. Firstly, syntactic N-best parsed trees are generated, and highly reliable sub-trees are obtained by computing their posterior probabilities and then initial reordering rule set is extracted according to the word alignment links and sub-trees. Two optimization strategies are utilized to process the initial rule set, namely the bilingually syntactic knowledge-based and probability threshold-based. Secondly, in order to guarantee the local fluency of phrases, the phrase table is used to constrain the reordering only taking place between phrases rather than inside phrases. Finally, the optimized reordering rule set constrained by the phrase table is utilized to perform pre-reordering in source-side sentences. Experimental results on NIST 2005 and 2008 test sets show that the BLEU score improves 0.68 and 0.83 respectively compared to the baseline system.

Key words: statistical machine translation, pre-reordering model, N-best parsed tree, reordering rules, rule optimization