acl acl2013 acl2013-166 acl2013-166-reference knowledge-graph by maker-knowledge-mining
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Author: Fei Huang ; Cezar Pendus
Abstract: We present a simple yet effective approach to syntactic reordering for Statistical Machine Translation (SMT). Instead of solely relying on the top-1 best-matching rule for source sentence preordering, we generalize fully lexicalized rules into partially lexicalized and unlexicalized rules to broaden the rule coverage. Furthermore, , we consider multiple permutations of all the matching rules, and select the final reordering path based on the weighed sum of reordering probabilities of these rules. Our experiments in English-Chinese and English-Japanese translations demonstrate the effectiveness of the proposed approach: we observe consistent and significant improvement in translation quality across multiple test sets in both language pairs judged by both humans and automatic metric. 1