Better Addressing Word Deletion for Statistical Machine Translation
Word deletion(WD)problems have a critical impact on the adequacy of translation and can lead to poor comprehension of lexical meaning in the translation result.This paper studies how the word deletion problem can be handled in statistical machine translation(SMT)in detail.We classify this problem into desired and undesired word deletion based on spurious and meaningful words.Consequently,we propose four effective models to handle undesired word deletion.To evaluate word deletion problems,we develop an automatic evaluation metric that highly correlates with human judgement.Translation systems are simultaneously tuned for the proposed evaluation metric and BLEU using minimum error rate training(MERT).The experimental results demonstrate that our methods achieve significant improvements in word deletion problems on Chinese-to-English translation tasks.
machine translation word deletion automatic evaluation
Qiang Li Dongdong Zhang Mu Li Tong Xiao Jingbo Zhu
Northeastern University,Shenyang,China Microsoft Research Asia,Beijing,China
国际会议
第五届自然语言处理与中文计算会议(NLPCC-ICCPOL2016)
昆明
英文
1-12
2016-12-02（万方平台首次上网日期，不代表论文的发表时间）