Context Sensitive Word Deletion Model for Statistical Machine Translation
Word deletion(WD)errors can lead to poor comprehension of the meaning of source translated sentences in phrase-based statistical machine translation(SMT),and have a critical impact on the adequacy of the translation results generated by SMT systems.In this paper,first we classify the word deletion into two categories,wanted and unwanted word deletions.For these two kinds of word deletions,we propose a maximum entropy based word deletion model to improve the transla-tion quality in phrase-based SMT.Our proposed model are based on features automatically learned from a real-word bitext.In our experi-ments on Chinese-to-English news and web translation tasks,the results show that our approach is capable of generating more adequate transla-tions compared with the baseline system,and our proposed word deletion model yields a+0.99 BLEU improvement and a-2.20 TER reduction on the NIST machine translation evaluation corpora.
natural language processing statistical machine transla-tion word deletion
Qiang Li Yaqian Han Tong Xiao Jingbo Zhu
NiuTrans Laboratory,School of Computer Science and Engineering,Northeastern University,Shenyang,China
国内会议
第十六届全国计算语言学学术会议暨第五届基于自然标注大数据的自然语言处理国际学术研讨会
南京
英文
1-12
2017-10-13（万方平台首次上网日期，不代表论文的发表时间）