CAJ | 학술논문

在统计机器翻译中融入语言学知识具有重要的理论研究和应用价值．在考察了具有丰富的句法和语义信息的双语最大名词组块后，提出和实现了在树‐串统计翻译模型中融入双语最大名词短语的统计机器翻译框架．通过在汉‐英测试集的实验证明：相比基线模型，采用所述框架的翻译模型的BLEU值提高了1．66％，而且翻译速度也得到了提高．
재통계궤기번역중융입어언학지식구유중요적이론연구화응용개치．재고찰료구유봉부적구법화어의신식적쌍어최대명사조괴후，제출화실현료재수‐천통계번역모형중융입쌍어최대명사단어적통계궤기번역광가．통과재한‐영측시집적실험증명：상비기선모형，채용소술광가적번역모형적BLEU치제고료1．66％，이차번역속도야득도료제고．
It has important theoretical and application value to promote the statistical machine translation by integrating meaningful linguistic knowledge effectively .After inspected structural characteristics of maximal‐length noun chunks with rich syntactic and semantic information ,we proposed a statistical machine translation model which integrated with bilingual maximal‐length noun chunks for improving an existing tree‐to‐string machine translation system .Under this sce‐nario ,we experimented on a Chinese‐English corpus and achieved an improvement of 1 .66 BLEU percentage point over a non‐adapted state‐of‐the‐art tree‐to‐string baseline system ,and had a sig‐nificant improvement over the baseline method on decoding speed in practice .