Sentence-Level Paraphrasing for Machine Translation System Combination

• 作者: Junguo Zhu Muyun Yang Sheng Li Tiejun Zhao
• 来源期刊: 国际计算机前沿大会会议论文集
• 学科: 社会科学
• 关键词: MACHINE TRANSLATION System COMBINATION PARAPHRASING NEURAL network
• 年，卷（期）: 2016,（1）
• 页码范围: 156-158
• 页数: 3页
• 分类号: C5

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.