Sentence-Level Paraphrasing for Machine Translation System Combination  

作　　者：Junguo Zhu Muyun Yang Sheng Li Tiejun Zhao 

机构地区：[1]Computer Science and Technology,Harbin Institute of Technology,92 West Dazhi Street,Harbin 150001,China

出　　处：《国际计算机前沿大会会议论文集》2016年第1期156-158,共3页International Conference of Pioneering Computer Scientists, Engineers and Educators（ICPCSEE）

基　　金：This paper is supported by the project of Natural Science Foundation of China (Grant No. 61272384&61370170).

摘　　要：In this paper, we propose to enhance machine translation system combination (MTSC) with a sentence-level paraphrasing model trained by a neural network. This work extends the number of candidates in MTSC by paraphrasing the whole original MT translation sentences. First we train a neural paraphrasing model of Encoder-Decoder, and leverage the model to paraphrase the MT system outputs to generate synonymous candidates in the semantic space. Then we merge all of them into a single improved translation by a state-of-the-art system combination approach (MEMT) adding some new paraphrasing features. Our experimental results show a significant improvement of 0.28 BLEU points on the WMT2011 test data and 0.41 BLEU points without considering the out-of-vocabulary (OOV) words for the sentence-level paraphrasing model.

关 键 词：MACHINE TRANSLATION System COMBINATION PARAPHRASING NEURAL network 

分 类 号：C5[社会学]